I stored the raw files on Github, so I used RCurl with Wehrley’s method that utilizes read.csv to the fullest. It’s one of the best ways I’ve found to read in data and also set data-types at the same time. He’s done a great job on that function. The dataset contains one ID variable, one response variable and ten predictor variables.
library(RCurl,quietly = T)
library(tidyverse,quietly = T)
library(ggplot2,quietly = T)
library(gridExtra,quietly = T)
library(Amelia,quietly = T)
library(beanplot,quietly = T)
library(caret,quietly = T)
library(stringr,quietly = T)
library(party, quietly = T)
# library(rattle, quietly = T)
readData <- function(path.name, file.name, column.types, missing.types) {
gurl <- paste(path.name,file.name,sep="")
download.file(gurl,file.name,method="curl",quiet = T)
tbl_df(read.csv(file.name,colClasses=column.types,
na.strings=missing.types))
}
Titanic.path <- "https://raw.githubusercontent.com/rsangole/Titanic/master/"
train.data.file <- "train.csv"
test.data.file <- "test.csv"
missing.types <- c("NA", "")
train.column.types <- c('integer', # PassengerId
'factor', # Survived
'factor', # Pclass
'character', # Name
'factor', # Sex
'numeric', # Age
'integer', # SibSp
'integer', # Parch
'character', # Ticket
'numeric', # Fare
'character', # Cabin
'factor' # Embarked
)
test.column.types <- train.column.types[-2] # # no Survived column in test.csv
train.raw <- readData(Titanic.path, train.data.file,train.column.types,missing.types)
test.raw <- readData(Titanic.path, test.data.file,test.column.types,missing.types)
prep_data <- function(D) {
if (!is.null(D$Survived)) {
D$Survived <- factor(D$Survived,
levels = c(1, 0),
labels = c('Survived', 'Dead'))
}
D$Pclass <- factor(D$Pclass,
levels = c(1, 2, 3),
labels = c('P1', 'P2', 'P3'))
D$PassengerId <- NULL
D
}
train.raw <- prep_data(train.raw)
test.raw <- prep_data(test.raw)
str(train.raw)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 891 obs. of 11 variables:
$ Survived: Factor w/ 2 levels "Survived","Dead": 2 1 1 1 2 2 2 2 1 1 ...
$ Pclass : Factor w/ 3 levels "P1","P2","P3": 3 1 3 1 3 3 1 3 3 2 ...
$ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
$ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
$ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
$ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
$ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
$ Ticket : chr "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
$ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
$ Cabin : chr NA "C85" NA "C123" ...
$ Embarked: Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...
Quick investigation of missing values can be done using the complete.cases(), and more thorough graphical summary can be done using Amelia. Overall, 79% of the observations have some missing data.
#Complete cases (percentages)
round(prop.table(table(complete.cases(train.raw))),2)
FALSE TRUE
0.79 0.21
Amelia lets us graphically investigate which variables have missing data. purr::map_xxx() gives this same information numerically in a succint fashion.
missmap(train.raw, main='Missing Values Analysis using Amelia ordered by % missing', col=c('red', 'gray'),legend = F,rank.order = T)#Missing cases (numbers):
map_int(train.raw,~sum(is.na(.x)))Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin
0 0 0 0 177 0 0 0 0 687
Embarked
2
#Missing cases (percentages):
round(map_dbl(train.raw,~sum(is.na(.x))/length(.x)),2)Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin
0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.00 0.77
Embarked
0.00
Cabin has a large number of missing values (77% missing). Imputing this variable may prove challenging or even useless. Age (19.9% missing) and Embarked (0.2%) missing are much more managable.
The first step in the analysis is to explore the data numerically and graphically. I always split up my EDA investigation as follows:
This gives me a structured approach towards larger datasets. My professor at Northwestern taught me to always complete a thorough intimate numeric & graphical EDA on the data, no matter how large the data 1. Anscombe (1973) clearly shows the importance of graphical analyses.
Survived is the response variable. As we can see, a large majority of the passengers did not survive the accident. The response variable is a False/True boolean variable. Thus, the analysis techniques used later will be those appropriate for classification problems.
round(prop.table(table(train.raw$Survived)),2)
Survived Dead
0.38 0.62
The first step is to look at every variable available. I prefer using the ggplot2 framework for all the visuals.
Age seems to have a bimodal distribution - very young children, and then directly young adults to mid-age persons. The 2nd mode is right skewed with no obvious outliers.
Fare certainly shows many outliers beyond the ~$200 level. A majority of the fares are <$50, which makes sense since a majority of the travelers are bound to be in the 3rd passenger class.
p1 <- ggplot(data=train.raw,aes(x=Age))+geom_histogram(bins = 40)
p2 <- ggplot(data=train.raw,aes(x=Fare))+geom_histogram(bins = 40)
grid.arrange(p1,p2)As we can see, the median fare is $14.5, the mean is $32, but the max is $512. We’ll investigate winzorising this variable in the latter part. Perhaps a transformation will also help?
summary(train.raw$Fare) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 7.91 14.45 32.20 31.00 512.33
A ggplot command is iterated over for the categorical variables.2
Key takeways for the categorical variables:
Pclass: If you were traveling 1st class, you have the highest chance of survival. Could be indicative of preferential treatment to those who paid more, a less politically correct class-stratified society, as well as the fact that the 1st class passengers had cabins at the very top of the ship.Pclass: Persons traveling 3rd class had the highest fatality rate. 3rd class passengers had cabins deep in the ship. With the reasons give in (1), this could have contributed to the low survival rate.Sex: Males have a very high fatality rate. Seems like the ‘women and children’ first policy was followed during evacuation.SibSp & Parch: What’s interesting here is, for both these variables, at level 0, the fatality rate is higher. At levels 1+, the chances of survival are much better. Again, this could point to the ‘women and children’ policy being followed. (Or perhaps there weren’t as many families with children on board!)Embarked: Southampton has a higher fatality rate than Cherbourg or Queenstown. A cross-tabulation between Embarked and Pclass shows that 72% of the 3rd class passengers and 89% of the 2nd class passengers boarded at Southampton. This jives with the observation that 2nd and 3rd class passengers have higher fatality rates.get_legend<-function(myggplot){
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}
p <- lapply(X = c('Pclass','Sex','SibSp','Parch','Embarked'),
FUN = function(x) ggplot(data = train.raw)+
aes_string(x=x,fill='Survived')+
geom_bar(position="dodge")+
theme(legend.position="none"))
legend <- get_legend(ggplot(data = train.raw,aes(x=Pclass,fill=Survived))+geom_bar())
grid.arrange(p[[1]],p[[2]],p[[3]],p[[4]],p[[5]],legend,layout_matrix =
cbind(c(1,2,3),c(4,5,NA),c(6,6,6)),widths=c(3,3,1))# round(prop.table(table(train.raw$Embarked,train.raw$Pclass),margin = 2),2)Grouped boxplots are a common method of comparing distributions grouped by categorical variables. I find beanplots to be excellent complementary plots to boxplots (and in some cases, even better). They’re a bit tricky to read at first - since they are so underutilized - but just through one plot, a wealth of information can be extracted.3
Here is a comparison of the same information between a boxplot and a beanplot. What can we infer from the bean plot better?
ggplot(train.raw,aes(y=Age,x=Pclass))+geom_boxplot(aes(fill=Survived))+theme_bw()beanplot(Age~Survived*Pclass,side='b',train.raw,col=list('yellow','orange'),
border = c('yellow2','darkorange'),ll = 0.05,boxwex = .5,
main='Passenger survival by pclass and Age',xlab='Passenger Class',ylab='Age')
legend('topright', fill = c('yellow','orange'), legend = c("Dead", "Survived"),bty = 'n',cex = .8)A look into the SibSp and Parch variables shows something interesting. There are three regions one can identify:
SibSp<=3 and Parch<=3, there are better chances for survival.The grouping by Pclass reveals that all the large families were 3rd class travelers. Worse access to help… lowest chance for survival.
These could be simple rules either hard coded during model building: something along the lines of: IF (SibSp>3 OR Parch >3) THEN prediction = 0, or some derived variables can be created.
ggplot(train.raw,aes(y=SibSp,x=Parch))+
geom_jitter(aes(color=Survived,shape=Pclass))+
theme_bw()+
scale_shape(solid=F)+
geom_vline(xintercept = 3,color='darkblue',lty=3)+
geom_hline(yintercept = 3,color='darkblue',lty=3)Starting with the easier one first:
Embarked: The largest portion of the passengers embared at Southhampton. I’m replacing the NAs with the same. First, I create a new imputed training dataset.
summary(train.raw$Embarked) C Q S NA's
168 77 644 2
train.imp <- train.raw
train.imp$Embarked[is.na(train.imp$Embarked)]='S'Names, Titles & Age:
The names have titles embedded in the strings. I can extract these using regex. Master, Miss, Mr and Mrs are the most popular - no surprise there, with lots of other titles. Here’s the distribution of the titles by age. These can be used to impute the missing age values.
train.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = train.raw$Name)
train.raw$title <- as.factor(train.raw$title)
train.raw %>%
na.omit() %>%
group_by(title) %>%
dplyr::summarise(Count=n(), Median_Age=round(median(Age),0)) %>%
arrange(-Median_Age)ggplot(train.raw,aes(x=title,y=Age))+
stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
geom_jitter(shape=21,alpha=.6,col='blue')+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
labs(caption='Red points are median values')Grouping similar titles together, I’ve kept a few titles - Officer, Royalty, Mr, Mrs and Miss.
train.imp <- train.raw
train.imp$title <- as.character(train.imp$title)
train.imp$title[train.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
train.imp$title[train.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
train.imp$title[train.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
train.imp$title[train.imp$title %in% c('Ms','Mlle')] <- 'Miss'
train.imp$title <- as.factor(train.imp$title)
train.imp %>%
group_by(title) %>%
summarise(Median_Age=median(Age,na.rm = T))ggplot(train.imp,aes(x=title,y=Age))+
geom_jitter(shape=21,alpha=.6,col='blue')+
stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
labs(caption='Red points are median values')Now for the missing Age values. I’m trying out two strategies to impute age, just for kicks. First, a regression tree using the rpart method. 5-repeat 10-fold cross validation across a tuning grid of 20 values of maxdepth. RMSE stablizes at a depth of 14, with a value of 12.2.
age.predictors <- train.imp %>%
dplyr::select(-Survived,-Cabin,-Ticket,-Name) %>%
filter(complete.cases(.))
set.seed(1234)
ctrl <- trainControl(method = "boot",
repeats = 5,
number = 200
)
rpartGrid <- data.frame(maxdepth = seq(4,20,2))
rpartFit <- train(Age~.,
data=age.predictors,
method='rpart2',
trControl = ctrl,
tuneGrid = rpartGrid
)
rpartFitCART
712 samples
7 predictor
No pre-processing
Resampling: Bootstrapped (200 reps)
Summary of sample sizes: 712, 712, 712, 712, 712, 712, ...
Resampling results across tuning parameters:
maxdepth RMSE Rsquared
4 12.91352 0.2172555
6 12.56362 0.2600303
8 12.37466 0.2835666
10 12.28184 0.2961068
12 12.23967 0.3028092
14 12.23329 0.3046570
16 12.24043 0.3041673
18 12.23669 0.3045630
20 12.23821 0.3044234
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was maxdepth = 14.
plot(rpartFit)plot(rpartFit$finalModel,margin=0.02)
text(rpartFit$finalModel,cex=0.8)Another way is to run a randomforest with a search over values of mtry using 5-repeat 10-fold cross validation. As we can see mtry=4 is the optimal value which results in the lowest RMSE of 11.4; much better than the rpart model.
set.seed(1234)
rfGrid <- data.frame(mtry=seq(1,6,1))
ctrl <- trainControl(method = "repeatedcv",
repeats = 5
)
rfFit <- train(Age~.,
data=age.predictors,
method='rf',
trControl = ctrl,
tuneGrid = rfGrid)
rfFitRandom Forest
712 samples
7 predictor
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 642, 640, 642, 641, 640, 639, ...
Resampling results across tuning parameters:
mtry RMSE Rsquared
1 12.46449 0.3816241
2 11.33714 0.4192503
3 11.05166 0.4263448
4 11.04766 0.4217797
5 11.10717 0.4152716
6 11.20324 0.4066238
RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 4.
plot(rfFit)I’m going to use the randomForest model. Using the predict.train() to predict values of age and plug them back into the imputed data. You can see the blue points which are the imputed values of Age. What I noticed is that for all the titles, the imputed Age value seems to be distributed fairly well, except Master. For Master, the three imputed are definitely outliers. I’m going to force these to the median Age.
missing.age <- train.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
train.imp %>%
mutate(AgeMissing = is.na(Age),
Age = ifelse(AgeMissing,age.predicted,Age)) %>%
ggplot(aes(x=title,y=Age))+
stat_summary(aes(y = Age,group=1), fun.y=median, colour="red", geom="point",group=1)+
geom_jitter(aes(y=Age,col=AgeMissing),shape=2)+
theme_bw()+
theme(axis.text.x = element_text(angle = 45, hjust = 1),legend.position="none")+
labs(caption='Red points are median values')train.imp$Age[is.na(train.imp$Age)] <- age.predicted
train.imp$Age[train.imp$title=='Master' & train.imp$Age > 20] <- median(train.imp$Age[train.imp$title=='Master'],na.rm = T)Child?: Trying out two engineered variables here - is the passenger a child or not? Using Age=18 as a threshold. And is s/he close enough to be considered a adult by chance? Those between 16 and 18 could be mistaken for not being children. (My way of incorporating a fudge factor in the decision process of ladies & children first.)
train.imp$child <- 0
train.imp$child[train.imp$Age<18] <- 1
train.imp$almostadult <- as.numeric(between(train.imp$Age,16,18))Really young, or really old?: Really young ones and older folks would get priority perhaps. Creating two categorical binary variables for these conditions.
train.imp$Young <- ifelse(train.imp$Age<10,1,0)
train.imp$Seniors <- ifelse(train.imp$Age>60,1,0)Family related: Let’s also create some variables that talk about family sizes. What’s the total family size – continous variable TotalFam. Is the person single, part of a couple or a family? Three categorical variables for these.
train.imp$TotalFam <- train.imp$SibSp + train.imp$Parch + 1
# train.imp$LastName <- train.imp$Name %>% str_extract(pattern = '[a-zA-Z]+(?=,)')
# train.imp$FamSize <- paste0(train.imp$TotalFam,train.imp$LastName)
# train.imp$LastName <- NULL
train.imp$Name <- NULL
train.imp$LargeParCh <- as.numeric(train.imp$Parch>=3)
train.imp$LargeSibSp <- as.numeric(train.imp$SibSp>=3)
train.imp$Single <- ifelse(train.imp$TotalFam==1,1,0)
train.imp$Couple <- ifelse(train.imp$TotalFam==2,1,0)
train.imp$Family <- ifelse(train.imp$TotalFam>2,1,0)Cabin related: Extracting the cabin alphabet and number from the cabin variable. Since the cabin numbers could be ordered from left to right or top to bottom on the boat, perhaps only the 1st digit is significant. Also, some folks have more than 1 cabin. Wonder if that’s important. Since lots of unknowns in the Cabin variable, all NA values are replaced by ‘U’. Refering to the deck diagram, the topmost decks are A and B, which are closest to the lifeboats. Perhaps that’s important too. Here, I create a bunch of categorical variables based off the original Cabin, and then remove it from the dataset.
train.imp$CabinMissing <- as.numeric(is.na(train.raw$Cabin))
train.imp$CabinCode <- map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
train.imp$CabinCode[is.na(train.imp$CabinCode)] <- 'U'
train.imp$CabinNum <- as.numeric(map_chr(train.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
train.imp$CabinNum <- map_int(train.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
train.imp$CabinNum[is.na(train.imp$CabinNum)] <- 0
train.imp$TopDeck <- ifelse(train.imp$CabinCode %in% c('A','B'),1,0)
train.imp$MidDeck <- ifelse(train.imp$CabinCode %in% c('C','D'),1,0)
train.imp$LowerDeck <- ifelse(train.imp$TopDeck==0 & train.imp$MidDeck ==0 ,1,0)
train.imp$NumberofCabins <- map_int(train.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
train.imp$Cabin <- NULLTicket: Lastly, the ticket variable. I’m not sure what to make of it, so I’m keeping it for now, after cleaning it up a bit. A majority (80%) of the rows have unique (one) ticket. 14% rows have a duplicate ticket, perhaps indicating a family. A small number of rows have 3+ duplicates of the tickets.
train.imp$Ticket %>% table() %>% as.numeric() %>% table().
1 2 3 4 5 6 7
547 94 21 11 2 3 3
There seems to be a bit of a pattern here. Tickets starting with 1 are mostly 1st class, those starting with 2 are 2nd class, and 3 - 3rd class. But, I feel it’s a very loose association.
train.imp %>% group_by(Pclass) %>% dplyr::select(Ticket,Pclass) %>% sample_n(5)What I’m going to do is clean up the columns (remove special characters, spaces etc), then split the Ticket column into four: TicketChar, TicketNum,TicketNumLength, TicketNumStart. (Upon running the script a few times, I’ve decided to get rid of TicketNum, but I’m commenting the code for future ref). The TicketChar variable as this distribution:
train.imp %<>%
mutate(
Ticket = str_to_upper(Ticket) %>%
str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+'))
) %>%
mutate(
TicketChar = map_chr(.x=TicketChar,
.f=~str_split(string=.x, pattern = '',simplify = T)[1])
) %>%
mutate(
TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
)
train.imp$Ticket <- NULL
train.imp$TicketNum <- NULL
table(train.imp$TicketChar)
A C F L P S U W
29 47 7 4 65 65 661 13
table(train.imp$TicketNumLen)
1 3 4 5 6 7
6 7 165 246 423 44
table(train.imp$TicketNumStart)
0 1 2 3 4 5 6 7 8 9
6 231 230 365 15 9 14 15 3 3
The fare variable has one massive outlier. Winzorising this variable using the 95th percentile value as the cutoff.
ggplot(train.imp,aes(x=Fare,fill=Pclass))+geom_histogram()+facet_grid(Pclass~.)quantile(train.imp$Fare[train.imp$Pclass=='P1'],probs = c(.1,.25,.5,.75,.95)) 10% 25% 50% 75% 95%
26.55000 30.92395 60.28750 93.50000 232.52395
train.imp$Fare[train.imp$Fare>232] <- 232The dataset is now prepared for modeling. Here’s a quick review of the data so far. 29 variables in total.
train.imp %>% glimpse()Observations: 891
Variables: 29
$ Survived <fctr> Dead, Survived, Survived, Survived, Dead, Dead, Dead, Dead, Survive...
$ Pclass <fctr> P3, P1, P3, P1, P3, P3, P1, P3, P3, P2, P3, P1, P3, P3, P3, P2, P3,...
$ Sex <fctr> male, female, female, female, male, male, male, male, female, femal...
$ Age <dbl> 22.00000, 38.00000, 26.00000, 35.00000, 35.00000, 35.04936, 54.00000...
$ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1, 0, 0, 0, 0,...
$ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625, 21.0750, ...
$ Embarked <fctr> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q, S, S, C, S, S, Q...
$ title <fctr> Mr, Mrs, Miss, Mrs, Mr, Mr, Mr, Master, Mrs, Mrs, Miss, Miss, Mr, M...
$ child <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1,...
$ almostadult <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ Young <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Seniors <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ TotalFam <dbl> 2, 2, 1, 2, 1, 1, 1, 5, 3, 2, 3, 1, 1, 7, 1, 1, 6, 1, 2, 1, 1, 1, 1,...
$ LargeParCh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ LargeSibSp <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ Single <dbl> 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1,...
$ Couple <dbl> 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,...
$ Family <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,...
$ CabinMissing <dbl> 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ CabinCode <chr> "U", "C", "U", "C", "U", "U", "E", "U", "U", "U", "G", "C", "U", "U"...
$ CabinNum <dbl> 0, 8, 0, 1, 0, 0, 4, 0, 0, 0, 6, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0,...
$ TopDeck <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ MidDeck <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,...
$ LowerDeck <dbl> 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,...
$ NumberofCabins <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ TicketNumStart <dbl> 2, 1, 3, 1, 3, 3, 1, 3, 3, 2, 9, 1, 2, 3, 3, 2, 3, 2, 3, 2, 2, 2, 3,...
$ TicketNumLen <int> 5, 5, 7, 6, 6, 6, 5, 6, 6, 6, 4, 6, 4, 6, 6, 6, 6, 6, 6, 4, 6, 6, 6,...
$ TicketChar <chr> "A", "P", "S", "U", "U", "U", "U", "U", "U", "U", "P", "U", "A", "U"...
ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
verboseIter = T,
classProbs = TRUE,
summaryFunction = twoClassSummary
# sampling = 'smote'
)
xgbGrid <- expand.grid(
nrounds=c(2,3,4,5,6,7),
max_depth=c(2,3,4,5,6,7),
eta=c(0.3,0.5),
gamma=1,
colsample_bytree=1,
min_child_weight=1,
subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)variable 'Survived' is not a factor
xgbFit <- train(
x=Dtrain,
y=train.imp$Survived,
method = 'xgbTree',
trControl = ctrl,
# metric = "Kappa",
tuneGrid = xgbGrid,
verbose = TRUE
)The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
Aggregating results
Selecting tuning parameters
Fitting nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbFiteXtreme Gradient Boosting
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 801, 803, 802, 802, 802, 801, ...
Resampling results across tuning parameters:
eta max_depth nrounds ROC Sens Spec
0.3 2 2 0.8581505 0.6148067 0.9052660
0.3 2 3 0.8677307 0.7174454 0.8699394
0.3 2 4 0.8679025 0.7310084 0.8728485
0.3 2 5 0.8704769 0.7262689 0.8768687
0.3 2 6 0.8712057 0.7315630 0.8710303
0.3 2 7 0.8718286 0.7338655 0.8706667
0.3 3 2 0.8737598 0.7373782 0.8702963
0.3 3 3 0.8771590 0.7403025 0.8750303
0.3 3 4 0.8790200 0.7339160 0.8775623
0.3 3 5 0.8804902 0.7333109 0.8815690
0.3 3 6 0.8806026 0.7350420 0.8808620
0.3 3 7 0.8807379 0.7256975 0.8856027
0.3 4 2 0.8768955 0.7425882 0.8757710
0.3 4 3 0.8805986 0.7373277 0.8794141
0.3 4 4 0.8816917 0.7432101 0.8801145
0.3 4 5 0.8826447 0.7444538 0.8819259
0.3 4 6 0.8815038 0.7402185 0.8812121
0.3 4 7 0.8804470 0.7273109 0.8833872
0.3 5 2 0.8760521 0.7307059 0.8706397
0.3 5 3 0.8774392 0.7290084 0.8706532
0.3 5 4 0.8802163 0.7325210 0.8797845
0.3 5 5 0.8793293 0.7395294 0.8775960
0.3 5 6 0.8806162 0.7360336 0.8797778
0.3 5 7 0.8828496 0.7377143 0.8797778
0.3 6 2 0.8765941 0.7447059 0.8673535
0.3 6 3 0.8772791 0.7359832 0.8706599
0.3 6 4 0.8809166 0.7342857 0.8819057
0.3 6 5 0.8833376 0.7430084 0.8804646
0.3 6 6 0.8819891 0.7401008 0.8837508
0.3 6 7 0.8839215 0.7388908 0.8844916
0.3 7 2 0.8762051 0.7331092 0.8761010
0.3 7 3 0.8775272 0.7423866 0.8812189
0.3 7 4 0.8798993 0.7394286 0.8830370
0.3 7 5 0.8803574 0.7435798 0.8852323
0.3 7 6 0.8816307 0.7423866 0.8855960
0.3 7 7 0.8818346 0.7429748 0.8918047
0.5 2 2 0.8575588 0.6287899 0.8965320
0.5 2 3 0.8669722 0.7303529 0.8714074
0.5 2 4 0.8700492 0.7315126 0.8680943
0.5 2 5 0.8704436 0.7315126 0.8721145
0.5 2 6 0.8750264 0.7408571 0.8721145
0.5 2 7 0.8764070 0.7454790 0.8786734
0.5 3 2 0.8757504 0.7373950 0.8684714
0.5 3 3 0.8780924 0.7397479 0.8695556
0.5 3 4 0.8770632 0.7216134 0.8885320
0.5 3 5 0.8781472 0.7327059 0.8819663
0.5 3 6 0.8763049 0.7367227 0.8866734
0.5 3 7 0.8769986 0.7308235 0.8874209
0.5 4 2 0.8791924 0.7420000 0.8794074
0.5 4 3 0.8805028 0.7425714 0.8739327
0.5 4 4 0.8797489 0.7337983 0.8808889
0.5 4 5 0.8792675 0.7314454 0.8856027
0.5 4 6 0.8802721 0.7325042 0.8855960
0.5 4 7 0.8808258 0.7366218 0.8866936
0.5 5 2 0.8752125 0.7318319 0.8721010
0.5 5 3 0.8778833 0.7303025 0.8775690
0.5 5 4 0.8802324 0.7336807 0.8823030
0.5 5 5 0.8792583 0.7296471 0.8819394
0.5 5 6 0.8810007 0.7249412 0.8870438
0.5 5 7 0.8802394 0.7249244 0.8844916
0.5 6 2 0.8732269 0.7353613 0.8706465
0.5 6 3 0.8761028 0.7377143 0.8801347
0.5 6 4 0.8777128 0.7335462 0.8837845
0.5 6 5 0.8791022 0.7341176 0.8841684
0.5 6 6 0.8782975 0.7341176 0.8899731
0.5 6 7 0.8771589 0.7323866 0.8892458
0.5 7 2 0.8743506 0.7377815 0.8746330
0.5 7 3 0.8772219 0.7419160 0.8874007
0.5 7 4 0.8778936 0.7406723 0.8888687
0.5 7 5 0.8804557 0.7441513 0.8903300
0.5 7 6 0.8815295 0.7429580 0.8852189
0.5 7 7 0.8810468 0.7377311 0.8877576
Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter 'colsample_bytree' was held
constant at a value of 1
Tuning parameter 'min_child_weight' was held constant at a value of 1
Tuning
parameter 'subsample' was held constant at a value of 1
ROC was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree =
1, min_child_weight = 1 and subsample = 1.
plot(xgbFit)xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel)xgb.importance(feature_names = colnames(Dtrain),model = xgbFit$finalModel) %>%
xgb.ggplot.importance()densityplot(xgbFit,pch='|')predict(xgbFit,type = 'raw') -> train.Class
predict(xgbFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
verboseIter = T,
classProbs = TRUE,
summaryFunction = twoClassSummary,
sampling = 'smote'
)
xgbGrid <- expand.grid(
nrounds=c(2,3,4,5,6,7),
max_depth=c(2,3,4,5,6,7),
eta=c(0.3,0.5),
gamma=1,
colsample_bytree=1,
min_child_weight=1,
subsample=1
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)variable 'Survived' is not a factor
xgbsmoteFit <- train(
x=Dtrain,
y=train.imp$Survived,
method = 'xgbTree',
trControl = ctrl,
# metric = "Kappa",
tuneGrid = xgbGrid,
verbose = TRUE
)The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
Attaching package: ‘DMwR’
The following object is masked from ‘package:plyr’:
join
- Fold01.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep1: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep2: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep3: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep4: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold01.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold02.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold03.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold04.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold05.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold06.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold07.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold08.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold09.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.3, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=2, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=3, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=4, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=5, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=6, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
+ Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
- Fold10.Rep5: eta=0.5, max_depth=7, gamma=1, colsample_bytree=1, min_child_weight=1, subsample=1, nrounds=7
Aggregating results
Selecting tuning parameters
Fitting nrounds = 7, max_depth = 6, eta = 0.3, gamma = 1, colsample_bytree = 1, min_child_weight = 1, subsample = 1 on full training set
xgbsmoteFiteXtreme Gradient Boosting
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 802, 802, 802, 802, 802, 802, ...
Addtional sampling using SMOTE
Resampling results across tuning parameters:
eta max_depth nrounds ROC Sens Spec
0.3 2 2 0.8422230 0.6017983 0.9041886
0.3 2 3 0.8492899 0.6334454 0.9001886
0.3 2 4 0.8559285 0.6701345 0.8968822
0.3 2 5 0.8626094 0.6590252 0.9031044
0.3 2 6 0.8654252 0.6521345 0.9060135
0.3 2 7 0.8660548 0.6718824 0.9016296
0.3 3 2 0.8594054 0.6228067 0.9235017
0.3 3 3 0.8638679 0.5971429 0.9336768
0.3 3 4 0.8659124 0.6034622 0.9326263
0.3 3 5 0.8663275 0.6007563 0.9300404
0.3 3 6 0.8692959 0.6066218 0.9307879
0.3 3 7 0.8724934 0.6129412 0.9256700
0.3 4 2 0.8552456 0.5870756 0.9348148
0.3 4 3 0.8626897 0.5895462 0.9337104
0.3 4 4 0.8694388 0.5895294 0.9358923
0.3 4 5 0.8729766 0.5995126 0.9362559
0.3 4 6 0.8755678 0.6064370 0.9366128
0.3 4 7 0.8794687 0.6110252 0.9388081
0.3 5 2 0.8600815 0.5929748 0.9329832
0.3 5 3 0.8633800 0.5917983 0.9311515
0.3 5 4 0.8689604 0.6000000 0.9347946
0.3 5 5 0.8729573 0.6028739 0.9377104
0.3 5 6 0.8751460 0.6105042 0.9340673
0.3 5 7 0.8766301 0.6169580 0.9333199
0.3 6 2 0.8633920 0.6239664 0.9191380
0.3 6 3 0.8673741 0.6320504 0.9205859
0.3 6 4 0.8715988 0.6268235 0.9231178
0.3 6 5 0.8750872 0.6378992 0.9220471
0.3 6 6 0.8761488 0.6419832 0.9209495
0.3 6 7 0.8800014 0.6496134 0.9169630
0.3 7 2 0.8561804 0.6192269 0.9195017
0.3 7 3 0.8610940 0.6321008 0.9162222
0.3 7 4 0.8646876 0.6397143 0.9176970
0.3 7 5 0.8695185 0.6490924 0.9129428
0.3 7 6 0.8710938 0.6572269 0.9143973
0.3 7 7 0.8713630 0.6619496 0.9143906
0.5 2 2 0.8490018 0.6370252 0.9012323
0.5 2 3 0.8599325 0.6549748 0.9038047
0.5 2 4 0.8629421 0.6410756 0.9125320
0.5 2 5 0.8664901 0.6181849 0.9194815
0.5 2 6 0.8674602 0.6101176 0.9202424
0.5 2 7 0.8699278 0.6131933 0.9238451
0.5 3 2 0.8610398 0.6136471 0.9242290
0.5 3 3 0.8634444 0.5919160 0.9347744
0.5 3 4 0.8695581 0.6042521 0.9296700
0.5 3 5 0.8712536 0.5908571 0.9289495
0.5 3 6 0.8726467 0.5941681 0.9347946
0.5 3 7 0.8752367 0.6024370 0.9351515
0.5 4 2 0.8626870 0.6024706 0.9329966
0.5 4 3 0.8693973 0.5964034 0.9347879
0.5 4 4 0.8755566 0.6059496 0.9315219
0.5 4 5 0.8759294 0.6204874 0.9373401
0.5 4 6 0.8739551 0.6158824 0.9373401
0.5 4 7 0.8744622 0.6292269 0.9318855
0.5 5 2 0.8631468 0.6064874 0.9278923
0.5 5 3 0.8709386 0.6163361 0.9282357
0.5 5 4 0.8742147 0.6315126 0.9267879
0.5 5 5 0.8749953 0.6338992 0.9271582
0.5 5 6 0.8747302 0.6426723 0.9238788
0.5 5 7 0.8757683 0.6466891 0.9191515
0.5 6 2 0.8625161 0.6239832 0.9213199
0.5 6 3 0.8671210 0.6332605 0.9184108
0.5 6 4 0.8676388 0.6419496 0.9111246
0.5 6 5 0.8689798 0.6571765 0.9154949
0.5 6 6 0.8728196 0.6530756 0.9125926
0.5 6 7 0.8742729 0.6594958 0.9129495
0.5 7 2 0.8649452 0.6513950 0.9213064
0.5 7 3 0.8670273 0.6544034 0.9209562
0.5 7 4 0.8748885 0.6684370 0.9198855
0.5 7 5 0.8736510 0.6684034 0.9176835
0.5 7 6 0.8763594 0.6748403 0.9180337
0.5 7 7 0.8767229 0.6777983 0.9154949
Tuning parameter 'gamma' was held constant at a value of 1
Tuning parameter
was held constant at a value of 1
Tuning parameter 'subsample' was held constant at a value
of 1
ROC was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 7, max_depth = 6, eta = 0.3, gamma =
1, colsample_bytree = 1, min_child_weight = 1 and subsample = 1.
plot(xgbsmoteFit)xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel)xgb.importance(feature_names = colnames(Dtrain),model = xgbsmoteFit$finalModel) %>%
xgb.ggplot.importance()densityplot(xgbsmoteFit,pch='|')predict(xgbsmoteFit,type = 'raw') -> train.Class
predict(xgbsmoteFit,type = 'prob') -> train.Probs
histogram(~Survived+Dead,train.Probs)ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
verboseIter = T,
classProbs = TRUE,
summaryFunction = twoClassSummary,
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
search = 'random'
# sampling = 'smote'
)
gbmGrid <- expand.grid(
n.trees=c(500,700,900,1100),
interaction.depth=c(1,2,3),
shrinkage=c(0.1,0.01),
n.minobsinnode=10
)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)variable 'Survived' is not a factor
set.seed(1)
boostFit <- train(
x = Dtrain,
y = boost.train$Survived,
trControl=ctrl,
method='gbm',
tuneGrid=gbmGrid
)Loading required package: gbm
Loading required package: survival
Attaching package: ‘survival’
The following object is masked from ‘package:caret’:
cluster
Loading required package: splines
Loading required package: parallel
Loaded gbm 2.1.3
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3258 nan 0.0100 0.0027
2 1.3203 nan 0.0100 0.0028
3 1.3146 nan 0.0100 0.0027
4 1.3093 nan 0.0100 0.0026
5 1.3040 nan 0.0100 0.0026
6 1.2991 nan 0.0100 0.0025
7 1.2941 nan 0.0100 0.0025
8 1.2886 nan 0.0100 0.0023
9 1.2832 nan 0.0100 0.0024
10 1.2782 nan 0.0100 0.0023
20 1.2360 nan 0.0100 0.0019
40 1.1740 nan 0.0100 0.0013
60 1.1258 nan 0.0100 0.0009
80 1.0870 nan 0.0100 0.0008
100 1.0570 nan 0.0100 0.0006
120 1.0323 nan 0.0100 0.0005
140 1.0111 nan 0.0100 0.0003
160 0.9930 nan 0.0100 0.0003
180 0.9771 nan 0.0100 0.0003
200 0.9629 nan 0.0100 0.0002
220 0.9512 nan 0.0100 0.0002
240 0.9406 nan 0.0100 0.0002
260 0.9318 nan 0.0100 0.0001
280 0.9230 nan 0.0100 0.0001
300 0.9151 nan 0.0100 0.0001
320 0.9079 nan 0.0100 -0.0000
340 0.9022 nan 0.0100 0.0000
360 0.8961 nan 0.0100 0.0001
380 0.8906 nan 0.0100 -0.0001
400 0.8858 nan 0.0100 0.0000
420 0.8811 nan 0.0100 -0.0000
440 0.8767 nan 0.0100 0.0001
460 0.8725 nan 0.0100 0.0001
480 0.8687 nan 0.0100 0.0000
500 0.8647 nan 0.0100 -0.0000
520 0.8610 nan 0.0100 0.0000
540 0.8580 nan 0.0100 -0.0000
560 0.8546 nan 0.0100 -0.0000
580 0.8511 nan 0.0100 0.0000
600 0.8481 nan 0.0100 0.0000
620 0.8452 nan 0.0100 0.0001
640 0.8423 nan 0.0100 0.0000
660 0.8400 nan 0.0100 -0.0000
680 0.8374 nan 0.0100 -0.0000
700 0.8349 nan 0.0100 0.0000
720 0.8325 nan 0.0100 -0.0000
740 0.8303 nan 0.0100 -0.0001
760 0.8280 nan 0.0100 -0.0000
780 0.8259 nan 0.0100 0.0000
800 0.8238 nan 0.0100 -0.0000
820 0.8214 nan 0.0100 -0.0000
840 0.8193 nan 0.0100 -0.0000
860 0.8174 nan 0.0100 -0.0000
880 0.8157 nan 0.0100 -0.0000
900 0.8141 nan 0.0100 -0.0001
920 0.8125 nan 0.0100 -0.0000
940 0.8108 nan 0.0100 -0.0000
960 0.8089 nan 0.0100 -0.0000
980 0.8071 nan 0.0100 -0.0001
1000 0.8054 nan 0.0100 -0.0000
1020 0.8036 nan 0.0100 -0.0000
1040 0.8021 nan 0.0100 -0.0000
1060 0.8006 nan 0.0100 -0.0000
1080 0.7992 nan 0.0100 -0.0000
1100 0.7979 nan 0.0100 -0.0001
- Fold01.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3239 nan 0.0100 0.0036
2 1.3168 nan 0.0100 0.0034
3 1.3098 nan 0.0100 0.0034
4 1.3029 nan 0.0100 0.0036
5 1.2960 nan 0.0100 0.0031
6 1.2897 nan 0.0100 0.0031
7 1.2831 nan 0.0100 0.0031
8 1.2770 nan 0.0100 0.0031
9 1.2708 nan 0.0100 0.0031
10 1.2648 nan 0.0100 0.0031
20 1.2101 nan 0.0100 0.0023
40 1.1251 nan 0.0100 0.0018
60 1.0614 nan 0.0100 0.0013
80 1.0137 nan 0.0100 0.0006
100 0.9779 nan 0.0100 0.0007
120 0.9491 nan 0.0100 0.0004
140 0.9253 nan 0.0100 0.0004
160 0.9064 nan 0.0100 0.0002
180 0.8910 nan 0.0100 0.0002
200 0.8775 nan 0.0100 0.0003
220 0.8667 nan 0.0100 0.0002
240 0.8575 nan 0.0100 -0.0000
260 0.8485 nan 0.0100 0.0002
280 0.8407 nan 0.0100 0.0002
300 0.8328 nan 0.0100 0.0000
320 0.8258 nan 0.0100 -0.0001
340 0.8192 nan 0.0100 0.0001
360 0.8131 nan 0.0100 -0.0003
380 0.8072 nan 0.0100 -0.0000
400 0.8019 nan 0.0100 0.0000
420 0.7969 nan 0.0100 -0.0001
440 0.7923 nan 0.0100 -0.0001
460 0.7883 nan 0.0100 -0.0000
480 0.7846 nan 0.0100 -0.0000
500 0.7810 nan 0.0100 -0.0000
520 0.7773 nan 0.0100 -0.0001
540 0.7740 nan 0.0100 -0.0001
560 0.7702 nan 0.0100 -0.0000
580 0.7668 nan 0.0100 -0.0001
600 0.7635 nan 0.0100 -0.0001
620 0.7602 nan 0.0100 -0.0000
640 0.7568 nan 0.0100 -0.0001
660 0.7541 nan 0.0100 -0.0002
680 0.7514 nan 0.0100 -0.0000
700 0.7486 nan 0.0100 -0.0001
720 0.7460 nan 0.0100 -0.0000
740 0.7434 nan 0.0100 -0.0002
760 0.7410 nan 0.0100 0.0000
780 0.7378 nan 0.0100 -0.0001
800 0.7353 nan 0.0100 -0.0002
820 0.7329 nan 0.0100 -0.0000
840 0.7308 nan 0.0100 -0.0001
860 0.7285 nan 0.0100 0.0000
880 0.7264 nan 0.0100 -0.0000
900 0.7240 nan 0.0100 -0.0001
920 0.7221 nan 0.0100 -0.0001
940 0.7197 nan 0.0100 -0.0000
960 0.7176 nan 0.0100 -0.0001
980 0.7156 nan 0.0100 0.0000
1000 0.7135 nan 0.0100 -0.0000
1020 0.7113 nan 0.0100 -0.0001
1040 0.7093 nan 0.0100 -0.0001
1060 0.7076 nan 0.0100 -0.0001
1080 0.7057 nan 0.0100 -0.0002
1100 0.7037 nan 0.0100 -0.0001
- Fold01.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3231 nan 0.0100 0.0040
2 1.3150 nan 0.0100 0.0039
3 1.3068 nan 0.0100 0.0038
4 1.2990 nan 0.0100 0.0037
5 1.2914 nan 0.0100 0.0035
6 1.2843 nan 0.0100 0.0038
7 1.2771 nan 0.0100 0.0034
8 1.2696 nan 0.0100 0.0037
9 1.2627 nan 0.0100 0.0032
10 1.2558 nan 0.0100 0.0032
20 1.1946 nan 0.0100 0.0027
40 1.0991 nan 0.0100 0.0021
60 1.0308 nan 0.0100 0.0012
80 0.9804 nan 0.0100 0.0009
100 0.9408 nan 0.0100 0.0006
120 0.9115 nan 0.0100 0.0006
140 0.8876 nan 0.0100 0.0004
160 0.8686 nan 0.0100 0.0002
180 0.8521 nan 0.0100 0.0002
200 0.8376 nan 0.0100 0.0003
220 0.8248 nan 0.0100 -0.0000
240 0.8141 nan 0.0100 -0.0001
260 0.8043 nan 0.0100 -0.0000
280 0.7955 nan 0.0100 0.0001
300 0.7880 nan 0.0100 -0.0000
320 0.7806 nan 0.0100 0.0000
340 0.7743 nan 0.0100 -0.0000
360 0.7687 nan 0.0100 -0.0000
380 0.7630 nan 0.0100 -0.0001
400 0.7571 nan 0.0100 0.0001
420 0.7519 nan 0.0100 0.0001
440 0.7470 nan 0.0100 -0.0001
460 0.7424 nan 0.0100 -0.0001
480 0.7375 nan 0.0100 -0.0000
500 0.7327 nan 0.0100 -0.0001
520 0.7285 nan 0.0100 -0.0001
540 0.7244 nan 0.0100 0.0000
560 0.7202 nan 0.0100 -0.0001
580 0.7162 nan 0.0100 -0.0001
600 0.7122 nan 0.0100 -0.0000
620 0.7086 nan 0.0100 -0.0001
640 0.7053 nan 0.0100 -0.0001
660 0.7017 nan 0.0100 -0.0001
680 0.6982 nan 0.0100 -0.0001
700 0.6947 nan 0.0100 -0.0001
720 0.6914 nan 0.0100 -0.0000
740 0.6883 nan 0.0100 -0.0001
760 0.6848 nan 0.0100 -0.0001
780 0.6819 nan 0.0100 -0.0001
800 0.6793 nan 0.0100 -0.0001
820 0.6765 nan 0.0100 -0.0002
840 0.6740 nan 0.0100 -0.0001
860 0.6712 nan 0.0100 -0.0001
880 0.6691 nan 0.0100 -0.0001
900 0.6668 nan 0.0100 -0.0001
920 0.6640 nan 0.0100 -0.0001
940 0.6616 nan 0.0100 -0.0001
960 0.6589 nan 0.0100 -0.0000
980 0.6565 nan 0.0100 -0.0002
1000 0.6540 nan 0.0100 -0.0001
1020 0.6516 nan 0.0100 -0.0000
1040 0.6490 nan 0.0100 -0.0000
1060 0.6468 nan 0.0100 -0.0002
1080 0.6440 nan 0.0100 0.0000
1100 0.6417 nan 0.0100 -0.0001
- Fold01.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2729 nan 0.1000 0.0270
2 1.2315 nan 0.1000 0.0211
3 1.1980 nan 0.1000 0.0170
4 1.1673 nan 0.1000 0.0150
5 1.1433 nan 0.1000 0.0091
6 1.1200 nan 0.1000 0.0113
7 1.1017 nan 0.1000 0.0103
8 1.0840 nan 0.1000 0.0084
9 1.0682 nan 0.1000 0.0067
10 1.0527 nan 0.1000 0.0067
20 0.9580 nan 0.1000 0.0022
40 0.8807 nan 0.1000 -0.0003
60 0.8422 nan 0.1000 -0.0000
80 0.8192 nan 0.1000 0.0002
100 0.8026 nan 0.1000 -0.0003
120 0.7894 nan 0.1000 -0.0005
140 0.7782 nan 0.1000 -0.0006
160 0.7690 nan 0.1000 -0.0007
180 0.7635 nan 0.1000 -0.0004
200 0.7558 nan 0.1000 -0.0006
220 0.7498 nan 0.1000 -0.0010
240 0.7446 nan 0.1000 -0.0014
260 0.7375 nan 0.1000 0.0001
280 0.7330 nan 0.1000 -0.0002
300 0.7285 nan 0.1000 -0.0004
320 0.7253 nan 0.1000 -0.0011
340 0.7232 nan 0.1000 -0.0004
360 0.7191 nan 0.1000 -0.0006
380 0.7157 nan 0.1000 -0.0011
400 0.7115 nan 0.1000 -0.0007
420 0.7079 nan 0.1000 -0.0007
440 0.7054 nan 0.1000 -0.0011
460 0.7030 nan 0.1000 -0.0011
480 0.7004 nan 0.1000 -0.0011
500 0.6977 nan 0.1000 -0.0008
520 0.6957 nan 0.1000 -0.0008
540 0.6921 nan 0.1000 -0.0005
560 0.6895 nan 0.1000 -0.0008
580 0.6881 nan 0.1000 -0.0012
600 0.6863 nan 0.1000 -0.0007
620 0.6842 nan 0.1000 -0.0007
640 0.6822 nan 0.1000 -0.0009
660 0.6813 nan 0.1000 -0.0008
680 0.6775 nan 0.1000 -0.0007
700 0.6744 nan 0.1000 -0.0003
720 0.6722 nan 0.1000 -0.0008
740 0.6723 nan 0.1000 -0.0012
760 0.6691 nan 0.1000 -0.0012
780 0.6673 nan 0.1000 -0.0005
800 0.6655 nan 0.1000 -0.0011
820 0.6643 nan 0.1000 -0.0008
840 0.6610 nan 0.1000 -0.0004
860 0.6591 nan 0.1000 -0.0004
880 0.6569 nan 0.1000 -0.0006
900 0.6555 nan 0.1000 -0.0008
920 0.6546 nan 0.1000 -0.0008
940 0.6536 nan 0.1000 -0.0005
960 0.6519 nan 0.1000 -0.0010
980 0.6504 nan 0.1000 -0.0006
1000 0.6485 nan 0.1000 -0.0007
1020 0.6463 nan 0.1000 -0.0004
1040 0.6454 nan 0.1000 -0.0009
1060 0.6443 nan 0.1000 -0.0008
1080 0.6428 nan 0.1000 -0.0002
1100 0.6410 nan 0.1000 -0.0013
- Fold01.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2634 nan 0.1000 0.0317
2 1.2062 nan 0.1000 0.0286
3 1.1578 nan 0.1000 0.0246
4 1.1170 nan 0.1000 0.0192
5 1.0853 nan 0.1000 0.0165
6 1.0566 nan 0.1000 0.0137
7 1.0313 nan 0.1000 0.0103
8 1.0089 nan 0.1000 0.0113
9 0.9901 nan 0.1000 0.0080
10 0.9713 nan 0.1000 0.0079
20 0.8776 nan 0.1000 0.0020
40 0.8043 nan 0.1000 -0.0002
60 0.7684 nan 0.1000 -0.0029
80 0.7388 nan 0.1000 -0.0011
100 0.7184 nan 0.1000 -0.0027
120 0.6993 nan 0.1000 -0.0010
140 0.6846 nan 0.1000 -0.0006
160 0.6711 nan 0.1000 -0.0013
180 0.6576 nan 0.1000 -0.0006
200 0.6473 nan 0.1000 -0.0006
220 0.6330 nan 0.1000 -0.0020
240 0.6203 nan 0.1000 -0.0006
260 0.6074 nan 0.1000 -0.0010
280 0.5965 nan 0.1000 -0.0011
300 0.5862 nan 0.1000 -0.0004
320 0.5772 nan 0.1000 -0.0005
340 0.5695 nan 0.1000 -0.0013
360 0.5625 nan 0.1000 -0.0011
380 0.5543 nan 0.1000 -0.0018
400 0.5489 nan 0.1000 -0.0006
420 0.5418 nan 0.1000 -0.0002
440 0.5335 nan 0.1000 -0.0019
460 0.5252 nan 0.1000 -0.0009
480 0.5172 nan 0.1000 -0.0018
500 0.5105 nan 0.1000 -0.0012
520 0.5063 nan 0.1000 -0.0018
540 0.4998 nan 0.1000 -0.0008
560 0.4935 nan 0.1000 -0.0003
580 0.4891 nan 0.1000 -0.0010
600 0.4814 nan 0.1000 -0.0008
620 0.4774 nan 0.1000 -0.0009
640 0.4733 nan 0.1000 -0.0012
660 0.4697 nan 0.1000 -0.0016
680 0.4640 nan 0.1000 -0.0004
700 0.4592 nan 0.1000 -0.0009
720 0.4543 nan 0.1000 -0.0006
740 0.4493 nan 0.1000 -0.0011
760 0.4461 nan 0.1000 -0.0006
780 0.4421 nan 0.1000 -0.0006
800 0.4390 nan 0.1000 -0.0005
820 0.4351 nan 0.1000 -0.0015
840 0.4306 nan 0.1000 -0.0007
860 0.4276 nan 0.1000 -0.0008
880 0.4228 nan 0.1000 -0.0006
900 0.4190 nan 0.1000 -0.0008
920 0.4162 nan 0.1000 -0.0010
940 0.4132 nan 0.1000 -0.0009
960 0.4086 nan 0.1000 -0.0008
980 0.4061 nan 0.1000 -0.0012
1000 0.4030 nan 0.1000 -0.0012
1020 0.3990 nan 0.1000 -0.0011
1040 0.3967 nan 0.1000 -0.0009
1060 0.3941 nan 0.1000 -0.0010
1080 0.3912 nan 0.1000 -0.0009
1100 0.3892 nan 0.1000 -0.0010
- Fold01.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2563 nan 0.1000 0.0358
2 1.1929 nan 0.1000 0.0300
3 1.1428 nan 0.1000 0.0246
4 1.0966 nan 0.1000 0.0214
5 1.0585 nan 0.1000 0.0194
6 1.0238 nan 0.1000 0.0166
7 0.9955 nan 0.1000 0.0122
8 0.9727 nan 0.1000 0.0102
9 0.9511 nan 0.1000 0.0099
10 0.9378 nan 0.1000 0.0010
20 0.8411 nan 0.1000 0.0022
40 0.7644 nan 0.1000 0.0000
60 0.7272 nan 0.1000 -0.0021
80 0.6917 nan 0.1000 -0.0012
100 0.6629 nan 0.1000 -0.0008
120 0.6398 nan 0.1000 -0.0012
140 0.6170 nan 0.1000 -0.0007
160 0.5972 nan 0.1000 -0.0001
180 0.5803 nan 0.1000 -0.0006
200 0.5631 nan 0.1000 -0.0010
220 0.5480 nan 0.1000 -0.0011
240 0.5326 nan 0.1000 -0.0019
260 0.5209 nan 0.1000 -0.0012
280 0.5068 nan 0.1000 -0.0010
300 0.4943 nan 0.1000 -0.0009
320 0.4859 nan 0.1000 -0.0010
340 0.4740 nan 0.1000 -0.0005
360 0.4634 nan 0.1000 -0.0004
380 0.4522 nan 0.1000 -0.0021
400 0.4411 nan 0.1000 -0.0009
420 0.4341 nan 0.1000 -0.0008
440 0.4265 nan 0.1000 -0.0009
460 0.4173 nan 0.1000 -0.0005
480 0.4100 nan 0.1000 -0.0016
500 0.4039 nan 0.1000 -0.0012
520 0.3978 nan 0.1000 -0.0008
540 0.3921 nan 0.1000 -0.0013
560 0.3841 nan 0.1000 -0.0011
580 0.3765 nan 0.1000 -0.0012
600 0.3672 nan 0.1000 -0.0008
620 0.3623 nan 0.1000 -0.0014
640 0.3552 nan 0.1000 -0.0005
660 0.3507 nan 0.1000 -0.0005
680 0.3473 nan 0.1000 -0.0006
700 0.3405 nan 0.1000 -0.0011
720 0.3354 nan 0.1000 -0.0003
740 0.3300 nan 0.1000 -0.0018
760 0.3269 nan 0.1000 -0.0007
780 0.3234 nan 0.1000 -0.0009
800 0.3175 nan 0.1000 -0.0009
820 0.3132 nan 0.1000 -0.0007
840 0.3087 nan 0.1000 -0.0004
860 0.3045 nan 0.1000 -0.0007
880 0.3008 nan 0.1000 -0.0011
900 0.2973 nan 0.1000 -0.0008
920 0.2940 nan 0.1000 -0.0004
940 0.2906 nan 0.1000 -0.0006
960 0.2859 nan 0.1000 -0.0009
980 0.2829 nan 0.1000 -0.0013
1000 0.2786 nan 0.1000 -0.0014
1020 0.2764 nan 0.1000 -0.0005
1040 0.2725 nan 0.1000 -0.0007
1060 0.2695 nan 0.1000 -0.0002
1080 0.2658 nan 0.1000 -0.0005
1100 0.2637 nan 0.1000 -0.0011
- Fold01.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3268 nan 0.0100 0.0029
2 1.3211 nan 0.0100 0.0028
3 1.3153 nan 0.0100 0.0027
4 1.3100 nan 0.0100 0.0027
5 1.3045 nan 0.0100 0.0027
6 1.2989 nan 0.0100 0.0025
7 1.2932 nan 0.0100 0.0025
8 1.2885 nan 0.0100 0.0025
9 1.2836 nan 0.0100 0.0025
10 1.2785 nan 0.0100 0.0024
20 1.2341 nan 0.0100 0.0019
40 1.1666 nan 0.0100 0.0013
60 1.1206 nan 0.0100 0.0010
80 1.0849 nan 0.0100 0.0007
100 1.0570 nan 0.0100 0.0006
120 1.0344 nan 0.0100 0.0005
140 1.0148 nan 0.0100 0.0004
160 0.9973 nan 0.0100 0.0003
180 0.9824 nan 0.0100 0.0002
200 0.9695 nan 0.0100 0.0002
220 0.9575 nan 0.0100 0.0002
240 0.9475 nan 0.0100 0.0002
260 0.9384 nan 0.0100 0.0001
280 0.9298 nan 0.0100 0.0001
300 0.9224 nan 0.0100 0.0001
320 0.9152 nan 0.0100 0.0001
340 0.9088 nan 0.0100 0.0001
360 0.9028 nan 0.0100 -0.0000
380 0.8972 nan 0.0100 0.0000
400 0.8921 nan 0.0100 0.0000
420 0.8872 nan 0.0100 0.0000
440 0.8829 nan 0.0100 -0.0000
460 0.8784 nan 0.0100 0.0001
480 0.8744 nan 0.0100 0.0000
500 0.8705 nan 0.0100 -0.0000
520 0.8667 nan 0.0100 0.0000
540 0.8631 nan 0.0100 0.0001
560 0.8598 nan 0.0100 0.0000
580 0.8564 nan 0.0100 -0.0000
600 0.8533 nan 0.0100 0.0000
620 0.8501 nan 0.0100 -0.0000
640 0.8471 nan 0.0100 0.0000
660 0.8445 nan 0.0100 -0.0001
680 0.8417 nan 0.0100 -0.0000
700 0.8391 nan 0.0100 -0.0000
720 0.8365 nan 0.0100 -0.0001
740 0.8342 nan 0.0100 0.0000
760 0.8317 nan 0.0100 0.0000
780 0.8294 nan 0.0100 -0.0000
800 0.8274 nan 0.0100 0.0000
820 0.8253 nan 0.0100 0.0000
840 0.8235 nan 0.0100 -0.0000
860 0.8217 nan 0.0100 -0.0000
880 0.8202 nan 0.0100 -0.0000
900 0.8184 nan 0.0100 -0.0000
920 0.8168 nan 0.0100 -0.0001
940 0.8149 nan 0.0100 0.0000
960 0.8134 nan 0.0100 -0.0000
980 0.8118 nan 0.0100 -0.0001
1000 0.8104 nan 0.0100 -0.0001
1020 0.8088 nan 0.0100 -0.0000
1040 0.8075 nan 0.0100 -0.0001
1060 0.8059 nan 0.0100 -0.0000
1080 0.8046 nan 0.0100 -0.0001
1100 0.8030 nan 0.0100 -0.0001
- Fold02.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3251 nan 0.0100 0.0035
2 1.3180 nan 0.0100 0.0034
3 1.3110 nan 0.0100 0.0032
4 1.3045 nan 0.0100 0.0032
5 1.2981 nan 0.0100 0.0030
6 1.2913 nan 0.0100 0.0032
7 1.2851 nan 0.0100 0.0032
8 1.2788 nan 0.0100 0.0030
9 1.2726 nan 0.0100 0.0028
10 1.2665 nan 0.0100 0.0030
20 1.2126 nan 0.0100 0.0025
40 1.1267 nan 0.0100 0.0018
60 1.0652 nan 0.0100 0.0012
80 1.0180 nan 0.0100 0.0010
100 0.9832 nan 0.0100 0.0007
120 0.9552 nan 0.0100 0.0006
140 0.9330 nan 0.0100 0.0002
160 0.9150 nan 0.0100 0.0002
180 0.9003 nan 0.0100 0.0001
200 0.8875 nan 0.0100 0.0001
220 0.8753 nan 0.0100 0.0003
240 0.8651 nan 0.0100 0.0000
260 0.8556 nan 0.0100 -0.0001
280 0.8472 nan 0.0100 0.0001
300 0.8384 nan 0.0100 0.0001
320 0.8308 nan 0.0100 0.0000
340 0.8238 nan 0.0100 0.0002
360 0.8175 nan 0.0100 -0.0000
380 0.8125 nan 0.0100 -0.0000
400 0.8069 nan 0.0100 0.0001
420 0.8021 nan 0.0100 0.0000
440 0.7978 nan 0.0100 0.0000
460 0.7920 nan 0.0100 -0.0000
480 0.7874 nan 0.0100 -0.0000
500 0.7834 nan 0.0100 -0.0001
520 0.7796 nan 0.0100 -0.0001
540 0.7757 nan 0.0100 -0.0000
560 0.7722 nan 0.0100 -0.0001
580 0.7686 nan 0.0100 0.0000
600 0.7650 nan 0.0100 -0.0000
620 0.7619 nan 0.0100 -0.0000
640 0.7587 nan 0.0100 0.0000
660 0.7558 nan 0.0100 -0.0002
680 0.7529 nan 0.0100 -0.0001
700 0.7502 nan 0.0100 -0.0001
720 0.7475 nan 0.0100 -0.0000
740 0.7449 nan 0.0100 -0.0001
760 0.7422 nan 0.0100 0.0000
780 0.7400 nan 0.0100 -0.0001
800 0.7371 nan 0.0100 -0.0001
820 0.7347 nan 0.0100 -0.0000
840 0.7326 nan 0.0100 -0.0001
860 0.7304 nan 0.0100 -0.0000
880 0.7281 nan 0.0100 -0.0001
900 0.7259 nan 0.0100 -0.0001
920 0.7239 nan 0.0100 -0.0000
940 0.7219 nan 0.0100 -0.0001
960 0.7197 nan 0.0100 -0.0001
980 0.7174 nan 0.0100 -0.0002
1000 0.7154 nan 0.0100 -0.0001
1020 0.7135 nan 0.0100 -0.0001
1040 0.7115 nan 0.0100 -0.0001
1060 0.7098 nan 0.0100 -0.0001
1080 0.7080 nan 0.0100 -0.0001
1100 0.7060 nan 0.0100 -0.0002
- Fold02.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3241 nan 0.0100 0.0039
2 1.3167 nan 0.0100 0.0041
3 1.3088 nan 0.0100 0.0035
4 1.3015 nan 0.0100 0.0037
5 1.2941 nan 0.0100 0.0034
6 1.2866 nan 0.0100 0.0036
7 1.2794 nan 0.0100 0.0032
8 1.2728 nan 0.0100 0.0034
9 1.2659 nan 0.0100 0.0033
10 1.2598 nan 0.0100 0.0031
20 1.2008 nan 0.0100 0.0024
40 1.1071 nan 0.0100 0.0019
60 1.0395 nan 0.0100 0.0014
80 0.9877 nan 0.0100 0.0011
100 0.9473 nan 0.0100 0.0007
120 0.9171 nan 0.0100 0.0005
140 0.8944 nan 0.0100 0.0003
160 0.8757 nan 0.0100 0.0002
180 0.8577 nan 0.0100 0.0001
200 0.8432 nan 0.0100 0.0003
220 0.8313 nan 0.0100 0.0001
240 0.8202 nan 0.0100 0.0000
260 0.8102 nan 0.0100 0.0001
280 0.8021 nan 0.0100 -0.0001
300 0.7945 nan 0.0100 0.0000
320 0.7869 nan 0.0100 0.0001
340 0.7797 nan 0.0100 -0.0000
360 0.7730 nan 0.0100 -0.0001
380 0.7663 nan 0.0100 0.0001
400 0.7604 nan 0.0100 -0.0002
420 0.7553 nan 0.0100 -0.0001
440 0.7505 nan 0.0100 -0.0001
460 0.7453 nan 0.0100 -0.0000
480 0.7410 nan 0.0100 -0.0001
500 0.7369 nan 0.0100 -0.0001
520 0.7323 nan 0.0100 -0.0001
540 0.7281 nan 0.0100 -0.0001
560 0.7242 nan 0.0100 -0.0000
580 0.7207 nan 0.0100 -0.0000
600 0.7170 nan 0.0100 -0.0000
620 0.7129 nan 0.0100 -0.0001
640 0.7090 nan 0.0100 0.0000
660 0.7051 nan 0.0100 -0.0001
680 0.7016 nan 0.0100 -0.0001
700 0.6982 nan 0.0100 -0.0001
720 0.6949 nan 0.0100 -0.0001
740 0.6915 nan 0.0100 -0.0001
760 0.6882 nan 0.0100 0.0001
780 0.6848 nan 0.0100 -0.0001
800 0.6822 nan 0.0100 -0.0001
820 0.6795 nan 0.0100 -0.0002
840 0.6765 nan 0.0100 -0.0001
860 0.6736 nan 0.0100 -0.0001
880 0.6710 nan 0.0100 -0.0001
900 0.6676 nan 0.0100 -0.0001
920 0.6649 nan 0.0100 -0.0002
940 0.6624 nan 0.0100 -0.0001
960 0.6603 nan 0.0100 -0.0002
980 0.6571 nan 0.0100 -0.0001
1000 0.6542 nan 0.0100 -0.0001
1020 0.6515 nan 0.0100 -0.0001
1040 0.6488 nan 0.0100 -0.0000
1060 0.6461 nan 0.0100 -0.0001
1080 0.6433 nan 0.0100 -0.0002
1100 0.6408 nan 0.0100 -0.0000
- Fold02.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2806 nan 0.1000 0.0278
2 1.2270 nan 0.1000 0.0221
3 1.1911 nan 0.1000 0.0173
4 1.1624 nan 0.1000 0.0151
5 1.1382 nan 0.1000 0.0124
6 1.1151 nan 0.1000 0.0105
7 1.0999 nan 0.1000 0.0073
8 1.0833 nan 0.1000 0.0082
9 1.0689 nan 0.1000 0.0061
10 1.0540 nan 0.1000 0.0062
20 0.9681 nan 0.1000 0.0025
40 0.8885 nan 0.1000 -0.0005
60 0.8491 nan 0.1000 -0.0001
80 0.8278 nan 0.1000 -0.0019
100 0.8104 nan 0.1000 -0.0008
120 0.7983 nan 0.1000 -0.0008
140 0.7881 nan 0.1000 -0.0001
160 0.7799 nan 0.1000 -0.0006
180 0.7715 nan 0.1000 -0.0005
200 0.7656 nan 0.1000 -0.0010
220 0.7614 nan 0.1000 -0.0013
240 0.7567 nan 0.1000 -0.0008
260 0.7513 nan 0.1000 -0.0004
280 0.7468 nan 0.1000 -0.0003
300 0.7406 nan 0.1000 -0.0004
320 0.7367 nan 0.1000 -0.0012
340 0.7329 nan 0.1000 -0.0005
360 0.7292 nan 0.1000 -0.0011
380 0.7260 nan 0.1000 -0.0002
400 0.7223 nan 0.1000 -0.0012
420 0.7195 nan 0.1000 -0.0002
440 0.7158 nan 0.1000 -0.0006
460 0.7136 nan 0.1000 -0.0009
480 0.7095 nan 0.1000 -0.0005
500 0.7076 nan 0.1000 -0.0009
520 0.7050 nan 0.1000 -0.0010
540 0.7039 nan 0.1000 -0.0009
560 0.7016 nan 0.1000 -0.0004
580 0.6992 nan 0.1000 -0.0002
600 0.6965 nan 0.1000 -0.0003
620 0.6940 nan 0.1000 -0.0012
640 0.6929 nan 0.1000 -0.0008
660 0.6921 nan 0.1000 -0.0013
680 0.6890 nan 0.1000 -0.0004
700 0.6877 nan 0.1000 -0.0009
720 0.6859 nan 0.1000 -0.0004
740 0.6830 nan 0.1000 -0.0004
760 0.6826 nan 0.1000 -0.0009
780 0.6803 nan 0.1000 -0.0005
800 0.6782 nan 0.1000 -0.0004
820 0.6761 nan 0.1000 -0.0008
840 0.6747 nan 0.1000 -0.0011
860 0.6736 nan 0.1000 -0.0005
880 0.6737 nan 0.1000 -0.0014
900 0.6711 nan 0.1000 -0.0012
920 0.6685 nan 0.1000 -0.0009
940 0.6674 nan 0.1000 -0.0005
960 0.6651 nan 0.1000 -0.0007
980 0.6629 nan 0.1000 -0.0002
1000 0.6614 nan 0.1000 -0.0007
1020 0.6605 nan 0.1000 -0.0008
1040 0.6587 nan 0.1000 -0.0009
1060 0.6569 nan 0.1000 -0.0012
1080 0.6560 nan 0.1000 -0.0009
1100 0.6541 nan 0.1000 -0.0006
- Fold02.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2721 nan 0.1000 0.0278
2 1.2150 nan 0.1000 0.0265
3 1.1643 nan 0.1000 0.0240
4 1.1247 nan 0.1000 0.0192
5 1.0911 nan 0.1000 0.0142
6 1.0607 nan 0.1000 0.0160
7 1.0364 nan 0.1000 0.0107
8 1.0149 nan 0.1000 0.0107
9 0.9956 nan 0.1000 0.0098
10 0.9790 nan 0.1000 0.0074
20 0.8838 nan 0.1000 0.0002
40 0.8108 nan 0.1000 -0.0002
60 0.7701 nan 0.1000 -0.0008
80 0.7435 nan 0.1000 -0.0008
100 0.7207 nan 0.1000 -0.0007
120 0.7037 nan 0.1000 -0.0006
140 0.6905 nan 0.1000 -0.0009
160 0.6727 nan 0.1000 -0.0007
180 0.6577 nan 0.1000 -0.0005
200 0.6430 nan 0.1000 -0.0006
220 0.6324 nan 0.1000 -0.0011
240 0.6217 nan 0.1000 -0.0008
260 0.6123 nan 0.1000 -0.0004
280 0.6029 nan 0.1000 -0.0022
300 0.5940 nan 0.1000 -0.0017
320 0.5875 nan 0.1000 -0.0006
340 0.5789 nan 0.1000 -0.0021
360 0.5708 nan 0.1000 -0.0009
380 0.5634 nan 0.1000 -0.0016
400 0.5550 nan 0.1000 -0.0002
420 0.5508 nan 0.1000 -0.0010
440 0.5448 nan 0.1000 -0.0017
460 0.5356 nan 0.1000 -0.0003
480 0.5294 nan 0.1000 -0.0005
500 0.5239 nan 0.1000 -0.0009
520 0.5184 nan 0.1000 -0.0008
540 0.5126 nan 0.1000 -0.0016
560 0.5072 nan 0.1000 -0.0003
580 0.5019 nan 0.1000 -0.0008
600 0.4991 nan 0.1000 -0.0008
620 0.4950 nan 0.1000 -0.0011
640 0.4892 nan 0.1000 -0.0011
660 0.4836 nan 0.1000 -0.0010
680 0.4792 nan 0.1000 -0.0003
700 0.4727 nan 0.1000 -0.0007
720 0.4673 nan 0.1000 -0.0003
740 0.4619 nan 0.1000 -0.0006
760 0.4580 nan 0.1000 -0.0010
780 0.4559 nan 0.1000 -0.0003
800 0.4525 nan 0.1000 -0.0014
820 0.4477 nan 0.1000 -0.0008
840 0.4437 nan 0.1000 -0.0002
860 0.4407 nan 0.1000 -0.0011
880 0.4365 nan 0.1000 -0.0008
900 0.4330 nan 0.1000 -0.0010
920 0.4282 nan 0.1000 -0.0010
940 0.4251 nan 0.1000 -0.0005
960 0.4234 nan 0.1000 -0.0006
980 0.4192 nan 0.1000 -0.0009
1000 0.4163 nan 0.1000 -0.0011
1020 0.4127 nan 0.1000 -0.0009
1040 0.4090 nan 0.1000 -0.0002
1060 0.4049 nan 0.1000 -0.0006
1080 0.4020 nan 0.1000 -0.0003
1100 0.4001 nan 0.1000 -0.0009
- Fold02.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2604 nan 0.1000 0.0403
2 1.1932 nan 0.1000 0.0301
3 1.1410 nan 0.1000 0.0240
4 1.0995 nan 0.1000 0.0185
5 1.0619 nan 0.1000 0.0172
6 1.0349 nan 0.1000 0.0131
7 1.0073 nan 0.1000 0.0129
8 0.9814 nan 0.1000 0.0113
9 0.9645 nan 0.1000 0.0059
10 0.9460 nan 0.1000 0.0072
20 0.8404 nan 0.1000 0.0016
40 0.7585 nan 0.1000 -0.0008
60 0.7122 nan 0.1000 -0.0011
80 0.6823 nan 0.1000 -0.0009
100 0.6559 nan 0.1000 -0.0021
120 0.6369 nan 0.1000 -0.0015
140 0.6149 nan 0.1000 -0.0007
160 0.5966 nan 0.1000 -0.0016
180 0.5776 nan 0.1000 -0.0014
200 0.5608 nan 0.1000 -0.0021
220 0.5467 nan 0.1000 -0.0018
240 0.5335 nan 0.1000 -0.0016
260 0.5221 nan 0.1000 -0.0017
280 0.5072 nan 0.1000 -0.0003
300 0.4989 nan 0.1000 -0.0009
320 0.4875 nan 0.1000 -0.0013
340 0.4796 nan 0.1000 -0.0020
360 0.4669 nan 0.1000 -0.0015
380 0.4561 nan 0.1000 -0.0005
400 0.4474 nan 0.1000 -0.0005
420 0.4401 nan 0.1000 -0.0011
440 0.4317 nan 0.1000 -0.0015
460 0.4231 nan 0.1000 -0.0011
480 0.4165 nan 0.1000 -0.0008
500 0.4095 nan 0.1000 -0.0008
520 0.4027 nan 0.1000 -0.0009
540 0.3963 nan 0.1000 -0.0006
560 0.3890 nan 0.1000 -0.0009
580 0.3833 nan 0.1000 -0.0013
600 0.3769 nan 0.1000 -0.0010
620 0.3701 nan 0.1000 -0.0003
640 0.3637 nan 0.1000 -0.0006
660 0.3561 nan 0.1000 -0.0010
680 0.3535 nan 0.1000 -0.0010
700 0.3491 nan 0.1000 -0.0007
720 0.3454 nan 0.1000 -0.0010
740 0.3396 nan 0.1000 -0.0017
760 0.3360 nan 0.1000 -0.0010
780 0.3318 nan 0.1000 -0.0006
800 0.3278 nan 0.1000 -0.0009
820 0.3235 nan 0.1000 -0.0011
840 0.3178 nan 0.1000 -0.0008
860 0.3125 nan 0.1000 -0.0009
880 0.3094 nan 0.1000 -0.0007
900 0.3057 nan 0.1000 -0.0008
920 0.3020 nan 0.1000 -0.0009
940 0.2979 nan 0.1000 -0.0007
960 0.2951 nan 0.1000 -0.0014
980 0.2892 nan 0.1000 -0.0009
1000 0.2846 nan 0.1000 -0.0006
1020 0.2825 nan 0.1000 -0.0012
1040 0.2781 nan 0.1000 -0.0014
1060 0.2729 nan 0.1000 -0.0006
1080 0.2691 nan 0.1000 -0.0008
1100 0.2657 nan 0.1000 -0.0008
- Fold02.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3259 nan 0.0100 0.0033
2 1.3196 nan 0.0100 0.0033
3 1.3130 nan 0.0100 0.0033
4 1.3068 nan 0.0100 0.0031
5 1.3010 nan 0.0100 0.0031
6 1.2941 nan 0.0100 0.0030
7 1.2885 nan 0.0100 0.0030
8 1.2824 nan 0.0100 0.0029
9 1.2769 nan 0.0100 0.0029
10 1.2712 nan 0.0100 0.0028
20 1.2192 nan 0.0100 0.0023
40 1.1444 nan 0.0100 0.0016
60 1.0905 nan 0.0100 0.0011
80 1.0511 nan 0.0100 0.0008
100 1.0195 nan 0.0100 0.0006
120 0.9929 nan 0.0100 0.0006
140 0.9699 nan 0.0100 0.0005
160 0.9510 nan 0.0100 0.0003
180 0.9350 nan 0.0100 0.0002
200 0.9207 nan 0.0100 0.0002
220 0.9084 nan 0.0100 0.0002
240 0.8974 nan 0.0100 0.0002
260 0.8877 nan 0.0100 0.0002
280 0.8784 nan 0.0100 0.0002
300 0.8704 nan 0.0100 0.0001
320 0.8636 nan 0.0100 0.0001
340 0.8565 nan 0.0100 0.0001
360 0.8503 nan 0.0100 0.0000
380 0.8448 nan 0.0100 0.0001
400 0.8392 nan 0.0100 0.0000
420 0.8340 nan 0.0100 -0.0001
440 0.8295 nan 0.0100 0.0001
460 0.8250 nan 0.0100 0.0000
480 0.8210 nan 0.0100 -0.0000
500 0.8174 nan 0.0100 0.0000
520 0.8134 nan 0.0100 -0.0000
540 0.8096 nan 0.0100 -0.0000
560 0.8058 nan 0.0100 -0.0000
580 0.8023 nan 0.0100 0.0000
600 0.7988 nan 0.0100 0.0000
620 0.7957 nan 0.0100 -0.0000
640 0.7926 nan 0.0100 -0.0000
660 0.7896 nan 0.0100 0.0000
680 0.7868 nan 0.0100 -0.0000
700 0.7843 nan 0.0100 0.0000
720 0.7819 nan 0.0100 -0.0000
740 0.7795 nan 0.0100 0.0000
760 0.7774 nan 0.0100 -0.0000
780 0.7751 nan 0.0100 0.0000
800 0.7731 nan 0.0100 -0.0000
820 0.7710 nan 0.0100 -0.0000
840 0.7692 nan 0.0100 -0.0000
860 0.7672 nan 0.0100 -0.0000
880 0.7655 nan 0.0100 -0.0001
900 0.7636 nan 0.0100 -0.0001
920 0.7618 nan 0.0100 -0.0000
940 0.7602 nan 0.0100 -0.0000
960 0.7585 nan 0.0100 -0.0000
980 0.7568 nan 0.0100 -0.0000
1000 0.7552 nan 0.0100 -0.0000
1020 0.7535 nan 0.0100 -0.0001
1040 0.7520 nan 0.0100 -0.0001
1060 0.7505 nan 0.0100 -0.0000
1080 0.7491 nan 0.0100 -0.0000
1100 0.7476 nan 0.0100 -0.0001
- Fold03.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0041
2 1.3157 nan 0.0100 0.0040
3 1.3080 nan 0.0100 0.0038
4 1.3009 nan 0.0100 0.0038
5 1.2936 nan 0.0100 0.0038
6 1.2864 nan 0.0100 0.0037
7 1.2790 nan 0.0100 0.0037
8 1.2716 nan 0.0100 0.0036
9 1.2644 nan 0.0100 0.0033
10 1.2575 nan 0.0100 0.0034
20 1.1964 nan 0.0100 0.0026
40 1.1010 nan 0.0100 0.0020
60 1.0316 nan 0.0100 0.0015
80 0.9798 nan 0.0100 0.0011
100 0.9412 nan 0.0100 0.0008
120 0.9090 nan 0.0100 0.0006
140 0.8854 nan 0.0100 0.0004
160 0.8652 nan 0.0100 0.0002
180 0.8486 nan 0.0100 0.0002
200 0.8340 nan 0.0100 0.0002
220 0.8214 nan 0.0100 0.0003
240 0.8112 nan 0.0100 0.0001
260 0.8016 nan 0.0100 0.0001
280 0.7930 nan 0.0100 0.0001
300 0.7857 nan 0.0100 0.0000
320 0.7783 nan 0.0100 0.0000
340 0.7721 nan 0.0100 0.0000
360 0.7661 nan 0.0100 0.0000
380 0.7601 nan 0.0100 -0.0000
400 0.7546 nan 0.0100 -0.0002
420 0.7496 nan 0.0100 -0.0000
440 0.7452 nan 0.0100 -0.0001
460 0.7406 nan 0.0100 0.0000
480 0.7362 nan 0.0100 -0.0001
500 0.7322 nan 0.0100 0.0000
520 0.7280 nan 0.0100 -0.0001
540 0.7246 nan 0.0100 -0.0001
560 0.7214 nan 0.0100 -0.0000
580 0.7179 nan 0.0100 -0.0001
600 0.7147 nan 0.0100 -0.0001
620 0.7114 nan 0.0100 0.0000
640 0.7084 nan 0.0100 -0.0000
660 0.7058 nan 0.0100 -0.0000
680 0.7027 nan 0.0100 -0.0002
700 0.7000 nan 0.0100 -0.0000
720 0.6971 nan 0.0100 -0.0001
740 0.6946 nan 0.0100 -0.0001
760 0.6917 nan 0.0100 -0.0002
780 0.6892 nan 0.0100 -0.0002
800 0.6868 nan 0.0100 -0.0001
820 0.6847 nan 0.0100 -0.0001
840 0.6820 nan 0.0100 -0.0000
860 0.6796 nan 0.0100 -0.0001
880 0.6774 nan 0.0100 -0.0000
900 0.6751 nan 0.0100 -0.0001
920 0.6731 nan 0.0100 -0.0000
940 0.6708 nan 0.0100 0.0000
960 0.6689 nan 0.0100 -0.0001
980 0.6668 nan 0.0100 -0.0002
1000 0.6642 nan 0.0100 -0.0001
1020 0.6624 nan 0.0100 -0.0000
1040 0.6605 nan 0.0100 -0.0001
1060 0.6585 nan 0.0100 -0.0001
1080 0.6567 nan 0.0100 -0.0001
1100 0.6549 nan 0.0100 -0.0001
- Fold03.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3233 nan 0.0100 0.0042
2 1.3148 nan 0.0100 0.0041
3 1.3061 nan 0.0100 0.0039
4 1.2982 nan 0.0100 0.0039
5 1.2897 nan 0.0100 0.0038
6 1.2815 nan 0.0100 0.0039
7 1.2734 nan 0.0100 0.0038
8 1.2656 nan 0.0100 0.0037
9 1.2578 nan 0.0100 0.0038
10 1.2504 nan 0.0100 0.0035
20 1.1829 nan 0.0100 0.0029
40 1.0805 nan 0.0100 0.0021
60 1.0060 nan 0.0100 0.0015
80 0.9490 nan 0.0100 0.0010
100 0.9052 nan 0.0100 0.0007
120 0.8730 nan 0.0100 0.0004
140 0.8477 nan 0.0100 0.0005
160 0.8262 nan 0.0100 0.0002
180 0.8090 nan 0.0100 0.0002
200 0.7938 nan 0.0100 0.0002
220 0.7807 nan 0.0100 0.0001
240 0.7698 nan 0.0100 0.0001
260 0.7595 nan 0.0100 0.0000
280 0.7506 nan 0.0100 -0.0000
300 0.7421 nan 0.0100 0.0000
320 0.7343 nan 0.0100 -0.0001
340 0.7277 nan 0.0100 0.0001
360 0.7206 nan 0.0100 -0.0001
380 0.7147 nan 0.0100 -0.0001
400 0.7092 nan 0.0100 -0.0001
420 0.7046 nan 0.0100 -0.0001
440 0.6995 nan 0.0100 -0.0002
460 0.6940 nan 0.0100 0.0000
480 0.6896 nan 0.0100 -0.0001
500 0.6846 nan 0.0100 -0.0001
520 0.6802 nan 0.0100 -0.0001
540 0.6761 nan 0.0100 -0.0000
560 0.6718 nan 0.0100 -0.0002
580 0.6683 nan 0.0100 -0.0002
600 0.6644 nan 0.0100 -0.0001
620 0.6608 nan 0.0100 -0.0000
640 0.6574 nan 0.0100 -0.0001
660 0.6543 nan 0.0100 -0.0001
680 0.6507 nan 0.0100 -0.0001
700 0.6477 nan 0.0100 -0.0000
720 0.6451 nan 0.0100 -0.0002
740 0.6419 nan 0.0100 -0.0000
760 0.6391 nan 0.0100 -0.0001
780 0.6363 nan 0.0100 -0.0001
800 0.6333 nan 0.0100 -0.0002
820 0.6306 nan 0.0100 -0.0001
840 0.6281 nan 0.0100 -0.0001
860 0.6253 nan 0.0100 0.0000
880 0.6228 nan 0.0100 -0.0001
900 0.6202 nan 0.0100 -0.0003
920 0.6174 nan 0.0100 -0.0002
940 0.6150 nan 0.0100 -0.0001
960 0.6122 nan 0.0100 -0.0002
980 0.6093 nan 0.0100 -0.0001
1000 0.6072 nan 0.0100 -0.0001
1020 0.6050 nan 0.0100 -0.0000
1040 0.6025 nan 0.0100 -0.0001
1060 0.6006 nan 0.0100 -0.0001
1080 0.5982 nan 0.0100 -0.0001
1100 0.5962 nan 0.0100 -0.0001
- Fold03.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2656 nan 0.1000 0.0318
2 1.2097 nan 0.1000 0.0255
3 1.1668 nan 0.1000 0.0203
4 1.1322 nan 0.1000 0.0164
5 1.1061 nan 0.1000 0.0135
6 1.0796 nan 0.1000 0.0109
7 1.0587 nan 0.1000 0.0092
8 1.0414 nan 0.1000 0.0075
9 1.0241 nan 0.1000 0.0079
10 1.0081 nan 0.1000 0.0062
20 0.9136 nan 0.1000 0.0029
40 0.8359 nan 0.1000 -0.0001
60 0.7971 nan 0.1000 -0.0003
80 0.7710 nan 0.1000 -0.0003
100 0.7550 nan 0.1000 -0.0001
120 0.7400 nan 0.1000 -0.0008
140 0.7304 nan 0.1000 -0.0009
160 0.7227 nan 0.1000 -0.0009
180 0.7149 nan 0.1000 -0.0004
200 0.7079 nan 0.1000 -0.0009
220 0.7014 nan 0.1000 -0.0009
240 0.6952 nan 0.1000 -0.0010
260 0.6908 nan 0.1000 -0.0001
280 0.6864 nan 0.1000 -0.0007
300 0.6827 nan 0.1000 -0.0005
320 0.6783 nan 0.1000 -0.0002
340 0.6741 nan 0.1000 -0.0005
360 0.6706 nan 0.1000 -0.0010
380 0.6687 nan 0.1000 -0.0010
400 0.6650 nan 0.1000 -0.0008
420 0.6617 nan 0.1000 -0.0003
440 0.6590 nan 0.1000 -0.0006
460 0.6560 nan 0.1000 -0.0009
480 0.6532 nan 0.1000 -0.0010
500 0.6509 nan 0.1000 -0.0008
520 0.6484 nan 0.1000 -0.0015
540 0.6467 nan 0.1000 -0.0005
560 0.6444 nan 0.1000 -0.0005
580 0.6433 nan 0.1000 -0.0005
600 0.6416 nan 0.1000 -0.0003
620 0.6393 nan 0.1000 -0.0006
640 0.6377 nan 0.1000 -0.0006
660 0.6354 nan 0.1000 -0.0005
680 0.6347 nan 0.1000 -0.0015
700 0.6315 nan 0.1000 -0.0008
720 0.6300 nan 0.1000 -0.0009
740 0.6285 nan 0.1000 -0.0004
760 0.6267 nan 0.1000 -0.0008
780 0.6250 nan 0.1000 -0.0013
800 0.6240 nan 0.1000 -0.0008
820 0.6227 nan 0.1000 -0.0013
840 0.6221 nan 0.1000 -0.0003
860 0.6203 nan 0.1000 -0.0003
880 0.6185 nan 0.1000 -0.0007
900 0.6163 nan 0.1000 -0.0006
920 0.6155 nan 0.1000 -0.0006
940 0.6146 nan 0.1000 -0.0004
960 0.6130 nan 0.1000 -0.0010
980 0.6105 nan 0.1000 -0.0026
1000 0.6095 nan 0.1000 -0.0003
1020 0.6071 nan 0.1000 -0.0005
1040 0.6069 nan 0.1000 -0.0009
1060 0.6061 nan 0.1000 -0.0005
1080 0.6049 nan 0.1000 -0.0009
1100 0.6036 nan 0.1000 -0.0008
- Fold03.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2527 nan 0.1000 0.0364
2 1.1924 nan 0.1000 0.0273
3 1.1384 nan 0.1000 0.0262
4 1.0938 nan 0.1000 0.0223
5 1.0580 nan 0.1000 0.0164
6 1.0250 nan 0.1000 0.0140
7 0.9986 nan 0.1000 0.0131
8 0.9745 nan 0.1000 0.0108
9 0.9549 nan 0.1000 0.0086
10 0.9376 nan 0.1000 0.0093
20 0.8343 nan 0.1000 0.0020
40 0.7623 nan 0.1000 0.0002
60 0.7218 nan 0.1000 -0.0000
80 0.6951 nan 0.1000 -0.0005
100 0.6746 nan 0.1000 -0.0010
120 0.6558 nan 0.1000 -0.0009
140 0.6381 nan 0.1000 -0.0007
160 0.6203 nan 0.1000 -0.0006
180 0.6039 nan 0.1000 -0.0005
200 0.5935 nan 0.1000 -0.0013
220 0.5831 nan 0.1000 -0.0014
240 0.5722 nan 0.1000 -0.0008
260 0.5627 nan 0.1000 -0.0010
280 0.5535 nan 0.1000 -0.0011
300 0.5455 nan 0.1000 -0.0009
320 0.5369 nan 0.1000 -0.0010
340 0.5281 nan 0.1000 -0.0010
360 0.5203 nan 0.1000 -0.0004
380 0.5152 nan 0.1000 -0.0007
400 0.5038 nan 0.1000 -0.0010
420 0.4992 nan 0.1000 -0.0009
440 0.4895 nan 0.1000 -0.0002
460 0.4857 nan 0.1000 -0.0008
480 0.4795 nan 0.1000 -0.0005
500 0.4728 nan 0.1000 -0.0013
520 0.4651 nan 0.1000 -0.0002
540 0.4584 nan 0.1000 -0.0009
560 0.4525 nan 0.1000 -0.0013
580 0.4474 nan 0.1000 -0.0016
600 0.4444 nan 0.1000 -0.0007
620 0.4393 nan 0.1000 -0.0006
640 0.4349 nan 0.1000 -0.0010
660 0.4307 nan 0.1000 -0.0007
680 0.4261 nan 0.1000 -0.0006
700 0.4204 nan 0.1000 -0.0002
720 0.4146 nan 0.1000 -0.0015
740 0.4112 nan 0.1000 -0.0006
760 0.4073 nan 0.1000 -0.0006
780 0.4014 nan 0.1000 -0.0002
800 0.3981 nan 0.1000 -0.0018
820 0.3945 nan 0.1000 -0.0002
840 0.3926 nan 0.1000 -0.0012
860 0.3896 nan 0.1000 -0.0011
880 0.3858 nan 0.1000 -0.0004
900 0.3813 nan 0.1000 -0.0005
920 0.3779 nan 0.1000 -0.0009
940 0.3738 nan 0.1000 -0.0011
960 0.3692 nan 0.1000 -0.0010
980 0.3661 nan 0.1000 -0.0007
1000 0.3622 nan 0.1000 -0.0003
1020 0.3590 nan 0.1000 -0.0011
1040 0.3551 nan 0.1000 -0.0010
1060 0.3523 nan 0.1000 -0.0008
1080 0.3495 nan 0.1000 -0.0008
1100 0.3462 nan 0.1000 -0.0006
- Fold03.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2489 nan 0.1000 0.0413
2 1.1828 nan 0.1000 0.0333
3 1.1255 nan 0.1000 0.0280
4 1.0775 nan 0.1000 0.0234
5 1.0344 nan 0.1000 0.0197
6 1.0028 nan 0.1000 0.0159
7 0.9728 nan 0.1000 0.0141
8 0.9469 nan 0.1000 0.0104
9 0.9232 nan 0.1000 0.0102
10 0.9055 nan 0.1000 0.0077
20 0.7933 nan 0.1000 0.0008
40 0.7114 nan 0.1000 -0.0005
60 0.6677 nan 0.1000 -0.0009
80 0.6346 nan 0.1000 -0.0011
100 0.6109 nan 0.1000 -0.0008
120 0.5895 nan 0.1000 -0.0006
140 0.5658 nan 0.1000 -0.0020
160 0.5466 nan 0.1000 -0.0015
180 0.5271 nan 0.1000 -0.0006
200 0.5116 nan 0.1000 -0.0014
220 0.5014 nan 0.1000 -0.0017
240 0.4862 nan 0.1000 -0.0007
260 0.4716 nan 0.1000 -0.0005
280 0.4553 nan 0.1000 -0.0005
300 0.4435 nan 0.1000 -0.0013
320 0.4334 nan 0.1000 -0.0007
340 0.4254 nan 0.1000 -0.0006
360 0.4164 nan 0.1000 -0.0009
380 0.4086 nan 0.1000 -0.0010
400 0.4021 nan 0.1000 -0.0010
420 0.3956 nan 0.1000 -0.0014
440 0.3879 nan 0.1000 -0.0009
460 0.3808 nan 0.1000 -0.0007
480 0.3726 nan 0.1000 -0.0008
500 0.3671 nan 0.1000 -0.0013
520 0.3614 nan 0.1000 -0.0008
540 0.3548 nan 0.1000 -0.0008
560 0.3495 nan 0.1000 -0.0009
580 0.3427 nan 0.1000 -0.0014
600 0.3356 nan 0.1000 -0.0015
620 0.3299 nan 0.1000 -0.0005
640 0.3253 nan 0.1000 -0.0008
660 0.3190 nan 0.1000 -0.0008
680 0.3130 nan 0.1000 -0.0009
700 0.3069 nan 0.1000 -0.0008
720 0.3022 nan 0.1000 -0.0010
740 0.2969 nan 0.1000 -0.0010
760 0.2913 nan 0.1000 -0.0012
780 0.2859 nan 0.1000 -0.0013
800 0.2823 nan 0.1000 -0.0017
820 0.2774 nan 0.1000 -0.0006
840 0.2726 nan 0.1000 -0.0006
860 0.2698 nan 0.1000 -0.0007
880 0.2661 nan 0.1000 -0.0004
900 0.2609 nan 0.1000 -0.0006
920 0.2566 nan 0.1000 -0.0007
940 0.2529 nan 0.1000 -0.0009
960 0.2503 nan 0.1000 -0.0011
980 0.2466 nan 0.1000 -0.0009
1000 0.2427 nan 0.1000 -0.0006
1020 0.2399 nan 0.1000 -0.0002
1040 0.2372 nan 0.1000 -0.0010
1060 0.2340 nan 0.1000 -0.0006
1080 0.2316 nan 0.1000 -0.0011
1100 0.2283 nan 0.1000 -0.0006
- Fold03.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3258 nan 0.0100 0.0030
2 1.3199 nan 0.0100 0.0029
3 1.3142 nan 0.0100 0.0028
4 1.3086 nan 0.0100 0.0028
5 1.3028 nan 0.0100 0.0027
6 1.2978 nan 0.0100 0.0027
7 1.2927 nan 0.0100 0.0027
8 1.2873 nan 0.0100 0.0026
9 1.2816 nan 0.0100 0.0025
10 1.2769 nan 0.0100 0.0025
20 1.2322 nan 0.0100 0.0021
40 1.1615 nan 0.0100 0.0014
60 1.1119 nan 0.0100 0.0010
80 1.0753 nan 0.0100 0.0008
100 1.0442 nan 0.0100 0.0003
120 1.0193 nan 0.0100 0.0003
140 0.9973 nan 0.0100 0.0004
160 0.9784 nan 0.0100 0.0004
180 0.9629 nan 0.0100 0.0003
200 0.9488 nan 0.0100 0.0003
220 0.9367 nan 0.0100 0.0002
240 0.9253 nan 0.0100 0.0002
260 0.9153 nan 0.0100 0.0002
280 0.9061 nan 0.0100 0.0001
300 0.8980 nan 0.0100 0.0001
320 0.8905 nan 0.0100 0.0001
340 0.8838 nan 0.0100 0.0001
360 0.8774 nan 0.0100 0.0001
380 0.8721 nan 0.0100 0.0001
400 0.8669 nan 0.0100 -0.0001
420 0.8618 nan 0.0100 0.0000
440 0.8580 nan 0.0100 0.0000
460 0.8536 nan 0.0100 0.0000
480 0.8496 nan 0.0100 0.0000
500 0.8456 nan 0.0100 -0.0000
520 0.8418 nan 0.0100 -0.0000
540 0.8379 nan 0.0100 -0.0000
560 0.8344 nan 0.0100 0.0000
580 0.8312 nan 0.0100 0.0001
600 0.8281 nan 0.0100 -0.0000
620 0.8252 nan 0.0100 0.0000
640 0.8222 nan 0.0100 -0.0002
660 0.8194 nan 0.0100 0.0000
680 0.8169 nan 0.0100 -0.0000
700 0.8140 nan 0.0100 0.0000
720 0.8114 nan 0.0100 -0.0000
740 0.8090 nan 0.0100 -0.0000
760 0.8066 nan 0.0100 -0.0000
780 0.8042 nan 0.0100 0.0000
800 0.8022 nan 0.0100 -0.0001
820 0.8001 nan 0.0100 -0.0001
840 0.7981 nan 0.0100 0.0000
860 0.7961 nan 0.0100 -0.0001
880 0.7943 nan 0.0100 -0.0000
900 0.7925 nan 0.0100 -0.0000
920 0.7904 nan 0.0100 -0.0000
940 0.7884 nan 0.0100 -0.0000
960 0.7866 nan 0.0100 -0.0001
980 0.7850 nan 0.0100 -0.0001
1000 0.7831 nan 0.0100 -0.0000
1020 0.7815 nan 0.0100 -0.0000
1040 0.7799 nan 0.0100 -0.0000
1060 0.7783 nan 0.0100 -0.0001
1080 0.7767 nan 0.0100 -0.0002
1100 0.7752 nan 0.0100 -0.0001
- Fold04.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3247 nan 0.0100 0.0037
2 1.3173 nan 0.0100 0.0036
3 1.3100 nan 0.0100 0.0034
4 1.3028 nan 0.0100 0.0035
5 1.2955 nan 0.0100 0.0034
6 1.2885 nan 0.0100 0.0034
7 1.2816 nan 0.0100 0.0032
8 1.2756 nan 0.0100 0.0028
9 1.2692 nan 0.0100 0.0030
10 1.2628 nan 0.0100 0.0031
20 1.2041 nan 0.0100 0.0026
40 1.1156 nan 0.0100 0.0017
60 1.0503 nan 0.0100 0.0013
80 1.0005 nan 0.0100 0.0010
100 0.9623 nan 0.0100 0.0008
120 0.9337 nan 0.0100 0.0005
140 0.9103 nan 0.0100 0.0004
160 0.8913 nan 0.0100 0.0004
180 0.8751 nan 0.0100 0.0002
200 0.8603 nan 0.0100 0.0002
220 0.8490 nan 0.0100 0.0003
240 0.8383 nan 0.0100 0.0001
260 0.8278 nan 0.0100 0.0000
280 0.8194 nan 0.0100 0.0000
300 0.8122 nan 0.0100 0.0001
320 0.8049 nan 0.0100 0.0000
340 0.7985 nan 0.0100 -0.0000
360 0.7932 nan 0.0100 -0.0000
380 0.7878 nan 0.0100 0.0000
400 0.7825 nan 0.0100 0.0000
420 0.7771 nan 0.0100 0.0000
440 0.7719 nan 0.0100 -0.0000
460 0.7676 nan 0.0100 -0.0000
480 0.7634 nan 0.0100 0.0000
500 0.7593 nan 0.0100 0.0000
520 0.7555 nan 0.0100 -0.0001
540 0.7519 nan 0.0100 -0.0000
560 0.7488 nan 0.0100 -0.0000
580 0.7455 nan 0.0100 -0.0001
600 0.7420 nan 0.0100 -0.0002
620 0.7386 nan 0.0100 -0.0000
640 0.7356 nan 0.0100 -0.0001
660 0.7326 nan 0.0100 -0.0001
680 0.7298 nan 0.0100 -0.0001
700 0.7269 nan 0.0100 0.0000
720 0.7246 nan 0.0100 -0.0001
740 0.7220 nan 0.0100 -0.0001
760 0.7190 nan 0.0100 -0.0001
780 0.7167 nan 0.0100 -0.0001
800 0.7139 nan 0.0100 -0.0000
820 0.7114 nan 0.0100 -0.0000
840 0.7087 nan 0.0100 -0.0000
860 0.7065 nan 0.0100 -0.0000
880 0.7042 nan 0.0100 -0.0001
900 0.7018 nan 0.0100 -0.0001
920 0.7001 nan 0.0100 -0.0002
940 0.6979 nan 0.0100 -0.0001
960 0.6958 nan 0.0100 -0.0000
980 0.6938 nan 0.0100 -0.0001
1000 0.6919 nan 0.0100 -0.0001
1020 0.6900 nan 0.0100 -0.0001
1040 0.6876 nan 0.0100 -0.0000
1060 0.6858 nan 0.0100 -0.0001
1080 0.6836 nan 0.0100 -0.0001
1100 0.6816 nan 0.0100 -0.0001
- Fold04.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3238 nan 0.0100 0.0043
2 1.3157 nan 0.0100 0.0039
3 1.3077 nan 0.0100 0.0040
4 1.3002 nan 0.0100 0.0039
5 1.2925 nan 0.0100 0.0036
6 1.2848 nan 0.0100 0.0038
7 1.2776 nan 0.0100 0.0036
8 1.2701 nan 0.0100 0.0035
9 1.2632 nan 0.0100 0.0032
10 1.2563 nan 0.0100 0.0033
20 1.1918 nan 0.0100 0.0027
40 1.0936 nan 0.0100 0.0019
60 1.0215 nan 0.0100 0.0013
80 0.9682 nan 0.0100 0.0011
100 0.9273 nan 0.0100 0.0007
120 0.8956 nan 0.0100 0.0005
140 0.8725 nan 0.0100 0.0003
160 0.8528 nan 0.0100 0.0002
180 0.8363 nan 0.0100 0.0002
200 0.8216 nan 0.0100 0.0000
220 0.8099 nan 0.0100 -0.0001
240 0.7988 nan 0.0100 -0.0001
260 0.7894 nan 0.0100 -0.0001
280 0.7815 nan 0.0100 -0.0001
300 0.7733 nan 0.0100 0.0001
320 0.7658 nan 0.0100 0.0000
340 0.7593 nan 0.0100 -0.0000
360 0.7530 nan 0.0100 0.0000
380 0.7470 nan 0.0100 -0.0000
400 0.7416 nan 0.0100 -0.0001
420 0.7358 nan 0.0100 -0.0001
440 0.7304 nan 0.0100 -0.0000
460 0.7259 nan 0.0100 -0.0001
480 0.7205 nan 0.0100 -0.0001
500 0.7156 nan 0.0100 -0.0001
520 0.7116 nan 0.0100 -0.0001
540 0.7074 nan 0.0100 -0.0001
560 0.7041 nan 0.0100 -0.0001
580 0.7006 nan 0.0100 -0.0001
600 0.6962 nan 0.0100 -0.0001
620 0.6927 nan 0.0100 -0.0002
640 0.6888 nan 0.0100 -0.0001
660 0.6858 nan 0.0100 -0.0002
680 0.6824 nan 0.0100 -0.0001
700 0.6786 nan 0.0100 0.0000
720 0.6755 nan 0.0100 -0.0001
740 0.6719 nan 0.0100 -0.0001
760 0.6687 nan 0.0100 -0.0001
780 0.6657 nan 0.0100 -0.0000
800 0.6627 nan 0.0100 -0.0000
820 0.6596 nan 0.0100 -0.0001
840 0.6567 nan 0.0100 -0.0001
860 0.6530 nan 0.0100 -0.0001
880 0.6503 nan 0.0100 -0.0001
900 0.6474 nan 0.0100 -0.0000
920 0.6450 nan 0.0100 -0.0001
940 0.6425 nan 0.0100 -0.0002
960 0.6394 nan 0.0100 -0.0001
980 0.6370 nan 0.0100 -0.0000
1000 0.6345 nan 0.0100 -0.0000
1020 0.6318 nan 0.0100 -0.0001
1040 0.6294 nan 0.0100 -0.0001
1060 0.6268 nan 0.0100 -0.0000
1080 0.6242 nan 0.0100 -0.0001
1100 0.6216 nan 0.0100 -0.0001
- Fold04.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2725 nan 0.1000 0.0284
2 1.2293 nan 0.1000 0.0228
3 1.1911 nan 0.1000 0.0194
4 1.1588 nan 0.1000 0.0159
5 1.1294 nan 0.1000 0.0127
6 1.1093 nan 0.1000 0.0083
7 1.0895 nan 0.1000 0.0101
8 1.0705 nan 0.1000 0.0084
9 1.0540 nan 0.1000 0.0074
10 1.0383 nan 0.1000 0.0071
20 0.9426 nan 0.1000 0.0020
40 0.8649 nan 0.1000 0.0001
60 0.8269 nan 0.1000 -0.0003
80 0.8018 nan 0.1000 0.0004
100 0.7848 nan 0.1000 0.0000
120 0.7676 nan 0.1000 -0.0001
140 0.7556 nan 0.1000 -0.0003
160 0.7473 nan 0.1000 -0.0008
180 0.7387 nan 0.1000 -0.0005
200 0.7327 nan 0.1000 -0.0013
220 0.7253 nan 0.1000 -0.0003
240 0.7203 nan 0.1000 -0.0008
260 0.7141 nan 0.1000 -0.0006
280 0.7106 nan 0.1000 -0.0007
300 0.7049 nan 0.1000 -0.0006
320 0.6993 nan 0.1000 -0.0009
340 0.6956 nan 0.1000 -0.0009
360 0.6936 nan 0.1000 -0.0007
380 0.6883 nan 0.1000 -0.0004
400 0.6851 nan 0.1000 -0.0006
420 0.6823 nan 0.1000 -0.0010
440 0.6783 nan 0.1000 -0.0005
460 0.6758 nan 0.1000 -0.0009
480 0.6734 nan 0.1000 -0.0005
500 0.6715 nan 0.1000 -0.0010
520 0.6682 nan 0.1000 -0.0007
540 0.6663 nan 0.1000 -0.0012
560 0.6638 nan 0.1000 -0.0007
580 0.6622 nan 0.1000 -0.0010
600 0.6598 nan 0.1000 -0.0004
620 0.6583 nan 0.1000 -0.0011
640 0.6549 nan 0.1000 0.0001
660 0.6527 nan 0.1000 -0.0006
680 0.6510 nan 0.1000 -0.0017
700 0.6486 nan 0.1000 -0.0006
720 0.6470 nan 0.1000 -0.0008
740 0.6455 nan 0.1000 -0.0015
760 0.6435 nan 0.1000 -0.0009
780 0.6424 nan 0.1000 -0.0007
800 0.6416 nan 0.1000 -0.0006
820 0.6404 nan 0.1000 -0.0008
840 0.6390 nan 0.1000 -0.0003
860 0.6379 nan 0.1000 -0.0004
880 0.6359 nan 0.1000 -0.0013
900 0.6347 nan 0.1000 -0.0002
920 0.6326 nan 0.1000 -0.0014
940 0.6306 nan 0.1000 -0.0005
960 0.6288 nan 0.1000 -0.0010
980 0.6278 nan 0.1000 -0.0009
1000 0.6264 nan 0.1000 -0.0011
1020 0.6249 nan 0.1000 -0.0011
1040 0.6237 nan 0.1000 -0.0004
1060 0.6219 nan 0.1000 -0.0011
1080 0.6211 nan 0.1000 -0.0012
1100 0.6205 nan 0.1000 -0.0010
- Fold04.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2617 nan 0.1000 0.0355
2 1.2069 nan 0.1000 0.0276
3 1.1577 nan 0.1000 0.0234
4 1.1136 nan 0.1000 0.0214
5 1.0797 nan 0.1000 0.0173
6 1.0487 nan 0.1000 0.0153
7 1.0216 nan 0.1000 0.0127
8 0.9972 nan 0.1000 0.0105
9 0.9763 nan 0.1000 0.0078
10 0.9583 nan 0.1000 0.0073
20 0.8632 nan 0.1000 0.0004
40 0.7901 nan 0.1000 0.0001
60 0.7519 nan 0.1000 0.0001
80 0.7230 nan 0.1000 -0.0007
100 0.7031 nan 0.1000 -0.0007
120 0.6815 nan 0.1000 -0.0009
140 0.6652 nan 0.1000 -0.0007
160 0.6467 nan 0.1000 -0.0001
180 0.6343 nan 0.1000 -0.0014
200 0.6179 nan 0.1000 -0.0012
220 0.6062 nan 0.1000 -0.0005
240 0.5929 nan 0.1000 -0.0005
260 0.5824 nan 0.1000 -0.0008
280 0.5714 nan 0.1000 -0.0002
300 0.5636 nan 0.1000 -0.0008
320 0.5541 nan 0.1000 -0.0007
340 0.5442 nan 0.1000 -0.0004
360 0.5376 nan 0.1000 -0.0009
380 0.5286 nan 0.1000 -0.0012
400 0.5217 nan 0.1000 -0.0009
420 0.5157 nan 0.1000 -0.0013
440 0.5078 nan 0.1000 -0.0008
460 0.5023 nan 0.1000 -0.0005
480 0.4975 nan 0.1000 -0.0004
500 0.4911 nan 0.1000 -0.0009
520 0.4849 nan 0.1000 -0.0009
540 0.4788 nan 0.1000 -0.0007
560 0.4744 nan 0.1000 -0.0008
580 0.4678 nan 0.1000 -0.0010
600 0.4660 nan 0.1000 -0.0007
620 0.4621 nan 0.1000 -0.0008
640 0.4552 nan 0.1000 -0.0006
660 0.4499 nan 0.1000 -0.0010
680 0.4457 nan 0.1000 -0.0008
700 0.4404 nan 0.1000 -0.0008
720 0.4366 nan 0.1000 -0.0011
740 0.4315 nan 0.1000 -0.0010
760 0.4267 nan 0.1000 -0.0010
780 0.4223 nan 0.1000 -0.0006
800 0.4192 nan 0.1000 -0.0005
820 0.4128 nan 0.1000 -0.0007
840 0.4095 nan 0.1000 -0.0012
860 0.4059 nan 0.1000 -0.0007
880 0.4019 nan 0.1000 -0.0006
900 0.3977 nan 0.1000 -0.0006
920 0.3945 nan 0.1000 -0.0005
940 0.3903 nan 0.1000 -0.0007
960 0.3877 nan 0.1000 -0.0007
980 0.3845 nan 0.1000 -0.0012
1000 0.3807 nan 0.1000 -0.0004
1020 0.3773 nan 0.1000 -0.0006
1040 0.3751 nan 0.1000 -0.0009
1060 0.3713 nan 0.1000 -0.0007
1080 0.3695 nan 0.1000 -0.0003
1100 0.3661 nan 0.1000 -0.0008
- Fold04.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2523 nan 0.1000 0.0385
2 1.1856 nan 0.1000 0.0334
3 1.1299 nan 0.1000 0.0260
4 1.0882 nan 0.1000 0.0175
5 1.0480 nan 0.1000 0.0196
6 1.0168 nan 0.1000 0.0130
7 0.9879 nan 0.1000 0.0133
8 0.9596 nan 0.1000 0.0117
9 0.9381 nan 0.1000 0.0090
10 0.9230 nan 0.1000 0.0059
20 0.8198 nan 0.1000 0.0009
40 0.7392 nan 0.1000 -0.0000
60 0.6967 nan 0.1000 -0.0010
80 0.6633 nan 0.1000 -0.0016
100 0.6375 nan 0.1000 -0.0004
120 0.6155 nan 0.1000 -0.0014
140 0.5953 nan 0.1000 -0.0016
160 0.5782 nan 0.1000 -0.0024
180 0.5579 nan 0.1000 -0.0014
200 0.5431 nan 0.1000 -0.0010
220 0.5259 nan 0.1000 -0.0005
240 0.5142 nan 0.1000 -0.0026
260 0.5024 nan 0.1000 -0.0010
280 0.4902 nan 0.1000 -0.0007
300 0.4796 nan 0.1000 -0.0006
320 0.4668 nan 0.1000 -0.0008
340 0.4573 nan 0.1000 -0.0008
360 0.4453 nan 0.1000 -0.0013
380 0.4382 nan 0.1000 -0.0014
400 0.4272 nan 0.1000 -0.0010
420 0.4160 nan 0.1000 -0.0011
440 0.4081 nan 0.1000 -0.0009
460 0.4003 nan 0.1000 -0.0004
480 0.3927 nan 0.1000 -0.0014
500 0.3835 nan 0.1000 -0.0010
520 0.3761 nan 0.1000 -0.0008
540 0.3716 nan 0.1000 -0.0008
560 0.3681 nan 0.1000 -0.0014
580 0.3626 nan 0.1000 -0.0016
600 0.3552 nan 0.1000 -0.0017
620 0.3479 nan 0.1000 -0.0007
640 0.3420 nan 0.1000 -0.0009
660 0.3360 nan 0.1000 -0.0007
680 0.3305 nan 0.1000 -0.0015
700 0.3234 nan 0.1000 -0.0011
720 0.3179 nan 0.1000 -0.0011
740 0.3127 nan 0.1000 -0.0010
760 0.3072 nan 0.1000 -0.0007
780 0.3029 nan 0.1000 -0.0013
800 0.2999 nan 0.1000 -0.0002
820 0.2944 nan 0.1000 -0.0009
840 0.2904 nan 0.1000 -0.0007
860 0.2879 nan 0.1000 -0.0008
880 0.2853 nan 0.1000 -0.0015
900 0.2820 nan 0.1000 -0.0008
920 0.2781 nan 0.1000 -0.0012
940 0.2745 nan 0.1000 -0.0010
960 0.2699 nan 0.1000 -0.0005
980 0.2659 nan 0.1000 -0.0013
1000 0.2624 nan 0.1000 -0.0008
1020 0.2578 nan 0.1000 -0.0010
1040 0.2546 nan 0.1000 -0.0007
1060 0.2500 nan 0.1000 -0.0004
1080 0.2468 nan 0.1000 -0.0005
1100 0.2435 nan 0.1000 -0.0009
- Fold04.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3263 nan 0.0100 0.0030
2 1.3203 nan 0.0100 0.0029
3 1.3142 nan 0.0100 0.0029
4 1.3084 nan 0.0100 0.0028
5 1.3031 nan 0.0100 0.0028
6 1.2973 nan 0.0100 0.0027
7 1.2918 nan 0.0100 0.0028
8 1.2866 nan 0.0100 0.0027
9 1.2817 nan 0.0100 0.0026
10 1.2768 nan 0.0100 0.0025
20 1.2323 nan 0.0100 0.0021
40 1.1618 nan 0.0100 0.0015
60 1.1105 nan 0.0100 0.0010
80 1.0741 nan 0.0100 0.0008
100 1.0458 nan 0.0100 0.0006
120 1.0214 nan 0.0100 0.0005
140 1.0006 nan 0.0100 0.0004
160 0.9834 nan 0.0100 0.0003
180 0.9695 nan 0.0100 0.0003
200 0.9574 nan 0.0100 0.0003
220 0.9464 nan 0.0100 0.0002
240 0.9363 nan 0.0100 0.0001
260 0.9279 nan 0.0100 0.0002
280 0.9193 nan 0.0100 0.0002
300 0.9121 nan 0.0100 0.0001
320 0.9051 nan 0.0100 0.0000
340 0.8990 nan 0.0100 0.0001
360 0.8936 nan 0.0100 0.0001
380 0.8880 nan 0.0100 -0.0001
400 0.8827 nan 0.0100 -0.0000
420 0.8779 nan 0.0100 -0.0000
440 0.8731 nan 0.0100 0.0001
460 0.8687 nan 0.0100 0.0001
480 0.8649 nan 0.0100 0.0000
500 0.8611 nan 0.0100 -0.0000
520 0.8572 nan 0.0100 -0.0000
540 0.8536 nan 0.0100 -0.0001
560 0.8502 nan 0.0100 -0.0000
580 0.8471 nan 0.0100 -0.0000
600 0.8440 nan 0.0100 -0.0000
620 0.8411 nan 0.0100 -0.0000
640 0.8383 nan 0.0100 -0.0000
660 0.8356 nan 0.0100 -0.0000
680 0.8332 nan 0.0100 -0.0000
700 0.8308 nan 0.0100 0.0000
720 0.8284 nan 0.0100 -0.0001
740 0.8262 nan 0.0100 -0.0000
760 0.8239 nan 0.0100 -0.0000
780 0.8215 nan 0.0100 -0.0000
800 0.8195 nan 0.0100 -0.0000
820 0.8174 nan 0.0100 -0.0000
840 0.8155 nan 0.0100 -0.0000
860 0.8136 nan 0.0100 -0.0002
880 0.8119 nan 0.0100 0.0000
900 0.8101 nan 0.0100 -0.0001
920 0.8084 nan 0.0100 -0.0000
940 0.8068 nan 0.0100 -0.0002
960 0.8053 nan 0.0100 -0.0001
980 0.8039 nan 0.0100 -0.0001
1000 0.8024 nan 0.0100 -0.0000
1020 0.8009 nan 0.0100 -0.0002
1040 0.7995 nan 0.0100 -0.0000
1060 0.7982 nan 0.0100 -0.0001
1080 0.7968 nan 0.0100 -0.0001
1100 0.7955 nan 0.0100 -0.0002
- Fold05.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0036
2 1.3170 nan 0.0100 0.0036
3 1.3095 nan 0.0100 0.0035
4 1.3027 nan 0.0100 0.0035
5 1.2962 nan 0.0100 0.0034
6 1.2891 nan 0.0100 0.0030
7 1.2825 nan 0.0100 0.0035
8 1.2759 nan 0.0100 0.0033
9 1.2691 nan 0.0100 0.0031
10 1.2627 nan 0.0100 0.0030
20 1.2071 nan 0.0100 0.0023
40 1.1183 nan 0.0100 0.0016
60 1.0547 nan 0.0100 0.0013
80 1.0067 nan 0.0100 0.0009
100 0.9706 nan 0.0100 0.0008
120 0.9434 nan 0.0100 0.0006
140 0.9224 nan 0.0100 0.0001
160 0.9042 nan 0.0100 0.0003
180 0.8888 nan 0.0100 0.0002
200 0.8765 nan 0.0100 0.0001
220 0.8663 nan 0.0100 0.0001
240 0.8556 nan 0.0100 0.0002
260 0.8467 nan 0.0100 0.0002
280 0.8383 nan 0.0100 0.0000
300 0.8305 nan 0.0100 0.0001
320 0.8239 nan 0.0100 -0.0000
340 0.8173 nan 0.0100 0.0000
360 0.8113 nan 0.0100 -0.0000
380 0.8063 nan 0.0100 0.0000
400 0.8009 nan 0.0100 -0.0001
420 0.7964 nan 0.0100 0.0000
440 0.7915 nan 0.0100 -0.0000
460 0.7875 nan 0.0100 -0.0002
480 0.7837 nan 0.0100 -0.0000
500 0.7798 nan 0.0100 0.0001
520 0.7762 nan 0.0100 -0.0000
540 0.7725 nan 0.0100 -0.0001
560 0.7692 nan 0.0100 -0.0000
580 0.7658 nan 0.0100 -0.0000
600 0.7624 nan 0.0100 -0.0000
620 0.7595 nan 0.0100 -0.0000
640 0.7566 nan 0.0100 0.0000
660 0.7542 nan 0.0100 0.0000
680 0.7514 nan 0.0100 -0.0000
700 0.7486 nan 0.0100 -0.0000
720 0.7459 nan 0.0100 -0.0001
740 0.7431 nan 0.0100 -0.0000
760 0.7403 nan 0.0100 0.0000
780 0.7383 nan 0.0100 -0.0000
800 0.7356 nan 0.0100 0.0000
820 0.7334 nan 0.0100 -0.0002
840 0.7311 nan 0.0100 -0.0001
860 0.7291 nan 0.0100 -0.0001
880 0.7270 nan 0.0100 -0.0001
900 0.7250 nan 0.0100 -0.0001
920 0.7229 nan 0.0100 -0.0000
940 0.7210 nan 0.0100 -0.0001
960 0.7193 nan 0.0100 -0.0001
980 0.7174 nan 0.0100 -0.0000
1000 0.7151 nan 0.0100 -0.0000
1020 0.7131 nan 0.0100 -0.0001
1040 0.7112 nan 0.0100 -0.0001
1060 0.7090 nan 0.0100 -0.0001
1080 0.7070 nan 0.0100 -0.0001
1100 0.7054 nan 0.0100 -0.0001
- Fold05.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0040
2 1.3161 nan 0.0100 0.0041
3 1.3081 nan 0.0100 0.0040
4 1.3008 nan 0.0100 0.0042
5 1.2931 nan 0.0100 0.0038
6 1.2857 nan 0.0100 0.0033
7 1.2784 nan 0.0100 0.0033
8 1.2714 nan 0.0100 0.0032
9 1.2649 nan 0.0100 0.0030
10 1.2579 nan 0.0100 0.0033
20 1.1965 nan 0.0100 0.0027
40 1.0987 nan 0.0100 0.0020
60 1.0285 nan 0.0100 0.0014
80 0.9767 nan 0.0100 0.0011
100 0.9380 nan 0.0100 0.0006
120 0.9090 nan 0.0100 0.0005
140 0.8862 nan 0.0100 0.0004
160 0.8671 nan 0.0100 0.0003
180 0.8509 nan 0.0100 0.0002
200 0.8363 nan 0.0100 0.0002
220 0.8240 nan 0.0100 0.0002
240 0.8136 nan 0.0100 0.0002
260 0.8038 nan 0.0100 0.0001
280 0.7944 nan 0.0100 0.0001
300 0.7867 nan 0.0100 0.0000
320 0.7788 nan 0.0100 -0.0000
340 0.7723 nan 0.0100 0.0001
360 0.7668 nan 0.0100 -0.0000
380 0.7606 nan 0.0100 -0.0001
400 0.7548 nan 0.0100 -0.0000
420 0.7494 nan 0.0100 -0.0001
440 0.7442 nan 0.0100 0.0001
460 0.7394 nan 0.0100 -0.0001
480 0.7354 nan 0.0100 -0.0001
500 0.7313 nan 0.0100 -0.0001
520 0.7276 nan 0.0100 0.0000
540 0.7235 nan 0.0100 -0.0001
560 0.7194 nan 0.0100 -0.0001
580 0.7154 nan 0.0100 -0.0001
600 0.7121 nan 0.0100 -0.0001
620 0.7087 nan 0.0100 -0.0001
640 0.7054 nan 0.0100 -0.0001
660 0.7018 nan 0.0100 -0.0002
680 0.6986 nan 0.0100 -0.0001
700 0.6957 nan 0.0100 -0.0001
720 0.6925 nan 0.0100 -0.0001
740 0.6893 nan 0.0100 -0.0000
760 0.6864 nan 0.0100 -0.0002
780 0.6839 nan 0.0100 -0.0001
800 0.6811 nan 0.0100 -0.0002
820 0.6783 nan 0.0100 -0.0001
840 0.6757 nan 0.0100 -0.0002
860 0.6728 nan 0.0100 -0.0001
880 0.6703 nan 0.0100 -0.0000
900 0.6677 nan 0.0100 -0.0002
920 0.6646 nan 0.0100 -0.0001
940 0.6617 nan 0.0100 -0.0001
960 0.6590 nan 0.0100 -0.0001
980 0.6568 nan 0.0100 -0.0001
1000 0.6543 nan 0.0100 -0.0001
1020 0.6519 nan 0.0100 -0.0001
1040 0.6494 nan 0.0100 -0.0002
1060 0.6463 nan 0.0100 -0.0002
1080 0.6440 nan 0.0100 -0.0001
1100 0.6419 nan 0.0100 -0.0000
- Fold05.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2717 nan 0.1000 0.0276
2 1.2269 nan 0.1000 0.0221
3 1.1846 nan 0.1000 0.0198
4 1.1529 nan 0.1000 0.0161
5 1.1242 nan 0.1000 0.0133
6 1.1056 nan 0.1000 0.0109
7 1.0871 nan 0.1000 0.0087
8 1.0708 nan 0.1000 0.0069
9 1.0589 nan 0.1000 0.0050
10 1.0437 nan 0.1000 0.0075
20 0.9523 nan 0.1000 0.0026
40 0.8772 nan 0.1000 0.0002
60 0.8405 nan 0.1000 0.0003
80 0.8157 nan 0.1000 -0.0002
100 0.7981 nan 0.1000 -0.0008
120 0.7879 nan 0.1000 -0.0005
140 0.7762 nan 0.1000 -0.0007
160 0.7697 nan 0.1000 -0.0004
180 0.7637 nan 0.1000 -0.0004
200 0.7571 nan 0.1000 -0.0005
220 0.7513 nan 0.1000 -0.0010
240 0.7472 nan 0.1000 -0.0013
260 0.7419 nan 0.1000 -0.0005
280 0.7374 nan 0.1000 -0.0006
300 0.7345 nan 0.1000 -0.0005
320 0.7320 nan 0.1000 -0.0013
340 0.7266 nan 0.1000 -0.0007
360 0.7239 nan 0.1000 -0.0004
380 0.7215 nan 0.1000 -0.0016
400 0.7162 nan 0.1000 -0.0004
420 0.7136 nan 0.1000 -0.0004
440 0.7109 nan 0.1000 -0.0007
460 0.7087 nan 0.1000 -0.0004
480 0.7060 nan 0.1000 -0.0010
500 0.7039 nan 0.1000 -0.0008
520 0.7005 nan 0.1000 -0.0007
540 0.6980 nan 0.1000 -0.0008
560 0.6949 nan 0.1000 -0.0007
580 0.6917 nan 0.1000 -0.0005
600 0.6894 nan 0.1000 -0.0003
620 0.6883 nan 0.1000 -0.0012
640 0.6857 nan 0.1000 -0.0008
660 0.6836 nan 0.1000 -0.0006
680 0.6812 nan 0.1000 -0.0008
700 0.6795 nan 0.1000 -0.0005
720 0.6773 nan 0.1000 -0.0008
740 0.6757 nan 0.1000 -0.0006
760 0.6739 nan 0.1000 -0.0009
780 0.6715 nan 0.1000 -0.0006
800 0.6695 nan 0.1000 -0.0004
820 0.6680 nan 0.1000 -0.0004
840 0.6654 nan 0.1000 -0.0007
860 0.6648 nan 0.1000 -0.0006
880 0.6637 nan 0.1000 -0.0004
900 0.6617 nan 0.1000 -0.0011
920 0.6609 nan 0.1000 -0.0006
940 0.6589 nan 0.1000 -0.0006
960 0.6568 nan 0.1000 -0.0024
980 0.6561 nan 0.1000 -0.0005
1000 0.6542 nan 0.1000 -0.0005
1020 0.6530 nan 0.1000 -0.0011
1040 0.6511 nan 0.1000 -0.0010
1060 0.6495 nan 0.1000 -0.0010
1080 0.6481 nan 0.1000 -0.0010
1100 0.6462 nan 0.1000 -0.0004
- Fold05.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2572 nan 0.1000 0.0316
2 1.2014 nan 0.1000 0.0279
3 1.1536 nan 0.1000 0.0229
4 1.1141 nan 0.1000 0.0201
5 1.0768 nan 0.1000 0.0177
6 1.0484 nan 0.1000 0.0123
7 1.0239 nan 0.1000 0.0124
8 1.0006 nan 0.1000 0.0102
9 0.9837 nan 0.1000 0.0067
10 0.9652 nan 0.1000 0.0080
20 0.8737 nan 0.1000 0.0023
40 0.8024 nan 0.1000 0.0003
60 0.7663 nan 0.1000 0.0002
80 0.7408 nan 0.1000 -0.0006
100 0.7220 nan 0.1000 -0.0011
120 0.7029 nan 0.1000 -0.0000
140 0.6856 nan 0.1000 -0.0003
160 0.6718 nan 0.1000 -0.0009
180 0.6593 nan 0.1000 -0.0011
200 0.6483 nan 0.1000 -0.0005
220 0.6357 nan 0.1000 -0.0019
240 0.6244 nan 0.1000 -0.0007
260 0.6132 nan 0.1000 -0.0005
280 0.6058 nan 0.1000 -0.0009
300 0.5952 nan 0.1000 -0.0003
320 0.5856 nan 0.1000 -0.0008
340 0.5796 nan 0.1000 -0.0011
360 0.5741 nan 0.1000 -0.0012
380 0.5655 nan 0.1000 -0.0011
400 0.5599 nan 0.1000 -0.0015
420 0.5534 nan 0.1000 -0.0012
440 0.5468 nan 0.1000 -0.0013
460 0.5418 nan 0.1000 -0.0015
480 0.5358 nan 0.1000 -0.0007
500 0.5301 nan 0.1000 -0.0013
520 0.5244 nan 0.1000 -0.0008
540 0.5184 nan 0.1000 -0.0007
560 0.5127 nan 0.1000 -0.0008
580 0.5076 nan 0.1000 -0.0010
600 0.5018 nan 0.1000 -0.0010
620 0.4965 nan 0.1000 -0.0008
640 0.4910 nan 0.1000 -0.0010
660 0.4871 nan 0.1000 -0.0008
680 0.4812 nan 0.1000 -0.0005
700 0.4783 nan 0.1000 -0.0010
720 0.4726 nan 0.1000 -0.0011
740 0.4688 nan 0.1000 -0.0009
760 0.4658 nan 0.1000 -0.0006
780 0.4621 nan 0.1000 -0.0006
800 0.4574 nan 0.1000 -0.0004
820 0.4530 nan 0.1000 -0.0011
840 0.4486 nan 0.1000 -0.0010
860 0.4450 nan 0.1000 -0.0013
880 0.4413 nan 0.1000 -0.0007
900 0.4383 nan 0.1000 -0.0006
920 0.4336 nan 0.1000 -0.0006
940 0.4328 nan 0.1000 -0.0007
960 0.4291 nan 0.1000 -0.0007
980 0.4242 nan 0.1000 -0.0012
1000 0.4207 nan 0.1000 -0.0013
1020 0.4180 nan 0.1000 -0.0007
1040 0.4139 nan 0.1000 -0.0005
1060 0.4108 nan 0.1000 -0.0012
1080 0.4089 nan 0.1000 -0.0005
1100 0.4055 nan 0.1000 -0.0013
- Fold05.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2542 nan 0.1000 0.0367
2 1.1910 nan 0.1000 0.0302
3 1.1378 nan 0.1000 0.0245
4 1.0933 nan 0.1000 0.0219
5 1.0567 nan 0.1000 0.0191
6 1.0246 nan 0.1000 0.0152
7 0.9983 nan 0.1000 0.0114
8 0.9746 nan 0.1000 0.0104
9 0.9562 nan 0.1000 0.0088
10 0.9381 nan 0.1000 0.0088
20 0.8421 nan 0.1000 0.0009
40 0.7673 nan 0.1000 -0.0005
60 0.7151 nan 0.1000 0.0002
80 0.6821 nan 0.1000 -0.0012
100 0.6556 nan 0.1000 -0.0006
120 0.6350 nan 0.1000 -0.0009
140 0.6133 nan 0.1000 -0.0010
160 0.5964 nan 0.1000 -0.0016
180 0.5789 nan 0.1000 -0.0014
200 0.5674 nan 0.1000 -0.0006
220 0.5539 nan 0.1000 -0.0020
240 0.5419 nan 0.1000 -0.0014
260 0.5284 nan 0.1000 -0.0011
280 0.5199 nan 0.1000 -0.0012
300 0.5077 nan 0.1000 -0.0014
320 0.4984 nan 0.1000 -0.0020
340 0.4897 nan 0.1000 -0.0011
360 0.4785 nan 0.1000 -0.0009
380 0.4725 nan 0.1000 -0.0006
400 0.4629 nan 0.1000 -0.0018
420 0.4549 nan 0.1000 -0.0008
440 0.4441 nan 0.1000 -0.0016
460 0.4371 nan 0.1000 -0.0012
480 0.4268 nan 0.1000 -0.0009
500 0.4174 nan 0.1000 -0.0009
520 0.4078 nan 0.1000 -0.0005
540 0.4002 nan 0.1000 -0.0010
560 0.3910 nan 0.1000 -0.0005
580 0.3840 nan 0.1000 -0.0010
600 0.3767 nan 0.1000 -0.0017
620 0.3691 nan 0.1000 -0.0010
640 0.3629 nan 0.1000 -0.0009
660 0.3576 nan 0.1000 -0.0006
680 0.3519 nan 0.1000 -0.0012
700 0.3452 nan 0.1000 -0.0013
720 0.3391 nan 0.1000 -0.0008
740 0.3338 nan 0.1000 -0.0016
760 0.3304 nan 0.1000 -0.0010
780 0.3241 nan 0.1000 -0.0008
800 0.3201 nan 0.1000 -0.0006
820 0.3148 nan 0.1000 -0.0014
840 0.3104 nan 0.1000 -0.0008
860 0.3063 nan 0.1000 -0.0006
880 0.3036 nan 0.1000 -0.0012
900 0.3002 nan 0.1000 -0.0005
920 0.2951 nan 0.1000 -0.0015
940 0.2906 nan 0.1000 -0.0010
960 0.2861 nan 0.1000 -0.0004
980 0.2821 nan 0.1000 -0.0007
1000 0.2783 nan 0.1000 -0.0011
1020 0.2750 nan 0.1000 -0.0012
1040 0.2712 nan 0.1000 -0.0010
1060 0.2691 nan 0.1000 -0.0007
1080 0.2651 nan 0.1000 -0.0012
1100 0.2622 nan 0.1000 -0.0007
- Fold05.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3258 nan 0.0100 0.0029
2 1.3203 nan 0.0100 0.0028
3 1.3149 nan 0.0100 0.0028
4 1.3093 nan 0.0100 0.0027
5 1.3041 nan 0.0100 0.0027
6 1.2992 nan 0.0100 0.0026
7 1.2945 nan 0.0100 0.0025
8 1.2893 nan 0.0100 0.0025
9 1.2843 nan 0.0100 0.0024
10 1.2798 nan 0.0100 0.0024
20 1.2367 nan 0.0100 0.0020
40 1.1690 nan 0.0100 0.0014
60 1.1232 nan 0.0100 0.0010
80 1.0877 nan 0.0100 0.0008
100 1.0586 nan 0.0100 0.0006
120 1.0352 nan 0.0100 0.0005
140 1.0164 nan 0.0100 0.0004
160 0.9990 nan 0.0100 0.0002
180 0.9844 nan 0.0100 0.0003
200 0.9707 nan 0.0100 0.0002
220 0.9594 nan 0.0100 0.0002
240 0.9488 nan 0.0100 0.0001
260 0.9391 nan 0.0100 0.0001
280 0.9307 nan 0.0100 0.0001
300 0.9232 nan 0.0100 0.0001
320 0.9165 nan 0.0100 0.0001
340 0.9097 nan 0.0100 0.0001
360 0.9039 nan 0.0100 0.0001
380 0.8980 nan 0.0100 0.0000
400 0.8928 nan 0.0100 0.0001
420 0.8879 nan 0.0100 -0.0000
440 0.8833 nan 0.0100 0.0000
460 0.8786 nan 0.0100 0.0000
480 0.8742 nan 0.0100 -0.0000
500 0.8699 nan 0.0100 -0.0001
520 0.8664 nan 0.0100 0.0000
540 0.8627 nan 0.0100 -0.0001
560 0.8592 nan 0.0100 -0.0000
580 0.8560 nan 0.0100 -0.0000
600 0.8528 nan 0.0100 0.0000
620 0.8498 nan 0.0100 -0.0002
640 0.8468 nan 0.0100 0.0000
660 0.8443 nan 0.0100 -0.0001
680 0.8414 nan 0.0100 0.0000
700 0.8389 nan 0.0100 -0.0000
720 0.8365 nan 0.0100 0.0000
740 0.8340 nan 0.0100 0.0000
760 0.8317 nan 0.0100 -0.0000
780 0.8295 nan 0.0100 -0.0000
800 0.8274 nan 0.0100 -0.0001
820 0.8252 nan 0.0100 -0.0000
840 0.8232 nan 0.0100 0.0000
860 0.8212 nan 0.0100 -0.0001
880 0.8191 nan 0.0100 -0.0000
900 0.8174 nan 0.0100 -0.0000
920 0.8157 nan 0.0100 -0.0000
940 0.8142 nan 0.0100 0.0000
960 0.8125 nan 0.0100 0.0000
980 0.8108 nan 0.0100 -0.0001
1000 0.8093 nan 0.0100 -0.0000
1020 0.8078 nan 0.0100 -0.0000
1040 0.8064 nan 0.0100 -0.0000
1060 0.8050 nan 0.0100 -0.0000
1080 0.8039 nan 0.0100 -0.0000
1100 0.8027 nan 0.0100 -0.0001
- Fold06.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0036
2 1.3176 nan 0.0100 0.0036
3 1.3102 nan 0.0100 0.0034
4 1.3033 nan 0.0100 0.0032
5 1.2966 nan 0.0100 0.0034
6 1.2897 nan 0.0100 0.0031
7 1.2832 nan 0.0100 0.0029
8 1.2768 nan 0.0100 0.0030
9 1.2708 nan 0.0100 0.0031
10 1.2648 nan 0.0100 0.0030
20 1.2096 nan 0.0100 0.0024
40 1.1230 nan 0.0100 0.0018
60 1.0612 nan 0.0100 0.0011
80 1.0164 nan 0.0100 0.0009
100 0.9807 nan 0.0100 0.0005
120 0.9537 nan 0.0100 0.0003
140 0.9314 nan 0.0100 0.0003
160 0.9122 nan 0.0100 0.0003
180 0.8971 nan 0.0100 0.0004
200 0.8845 nan 0.0100 0.0001
220 0.8730 nan 0.0100 0.0001
240 0.8622 nan 0.0100 0.0000
260 0.8533 nan 0.0100 0.0000
280 0.8446 nan 0.0100 -0.0000
300 0.8371 nan 0.0100 0.0001
320 0.8296 nan 0.0100 0.0000
340 0.8231 nan 0.0100 0.0000
360 0.8175 nan 0.0100 -0.0000
380 0.8118 nan 0.0100 -0.0000
400 0.8066 nan 0.0100 -0.0000
420 0.8018 nan 0.0100 -0.0000
440 0.7975 nan 0.0100 -0.0000
460 0.7929 nan 0.0100 -0.0001
480 0.7884 nan 0.0100 -0.0000
500 0.7846 nan 0.0100 -0.0001
520 0.7810 nan 0.0100 -0.0000
540 0.7778 nan 0.0100 -0.0001
560 0.7750 nan 0.0100 -0.0000
580 0.7717 nan 0.0100 -0.0000
600 0.7683 nan 0.0100 -0.0000
620 0.7648 nan 0.0100 -0.0001
640 0.7620 nan 0.0100 -0.0001
660 0.7592 nan 0.0100 -0.0001
680 0.7566 nan 0.0100 -0.0002
700 0.7539 nan 0.0100 -0.0001
720 0.7517 nan 0.0100 -0.0001
740 0.7489 nan 0.0100 -0.0000
760 0.7461 nan 0.0100 -0.0000
780 0.7439 nan 0.0100 -0.0001
800 0.7419 nan 0.0100 -0.0002
820 0.7393 nan 0.0100 -0.0000
840 0.7367 nan 0.0100 -0.0001
860 0.7347 nan 0.0100 -0.0001
880 0.7325 nan 0.0100 -0.0000
900 0.7301 nan 0.0100 -0.0001
920 0.7279 nan 0.0100 -0.0001
940 0.7260 nan 0.0100 -0.0001
960 0.7240 nan 0.0100 -0.0001
980 0.7220 nan 0.0100 -0.0001
1000 0.7196 nan 0.0100 -0.0001
1020 0.7175 nan 0.0100 -0.0000
1040 0.7158 nan 0.0100 -0.0000
1060 0.7135 nan 0.0100 -0.0000
1080 0.7118 nan 0.0100 -0.0001
1100 0.7104 nan 0.0100 -0.0001
- Fold06.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3240 nan 0.0100 0.0036
2 1.3164 nan 0.0100 0.0037
3 1.3089 nan 0.0100 0.0037
4 1.3020 nan 0.0100 0.0036
5 1.2947 nan 0.0100 0.0034
6 1.2880 nan 0.0100 0.0032
7 1.2811 nan 0.0100 0.0036
8 1.2737 nan 0.0100 0.0035
9 1.2673 nan 0.0100 0.0030
10 1.2610 nan 0.0100 0.0031
20 1.1990 nan 0.0100 0.0026
40 1.1049 nan 0.0100 0.0018
60 1.0377 nan 0.0100 0.0014
80 0.9870 nan 0.0100 0.0011
100 0.9491 nan 0.0100 0.0008
120 0.9196 nan 0.0100 0.0004
140 0.8954 nan 0.0100 0.0003
160 0.8753 nan 0.0100 0.0004
180 0.8593 nan 0.0100 -0.0000
200 0.8462 nan 0.0100 0.0002
220 0.8337 nan 0.0100 -0.0000
240 0.8243 nan 0.0100 0.0001
260 0.8141 nan 0.0100 0.0002
280 0.8054 nan 0.0100 0.0000
300 0.7972 nan 0.0100 0.0000
320 0.7899 nan 0.0100 0.0001
340 0.7832 nan 0.0100 0.0000
360 0.7771 nan 0.0100 -0.0001
380 0.7702 nan 0.0100 0.0001
400 0.7648 nan 0.0100 0.0001
420 0.7591 nan 0.0100 -0.0001
440 0.7538 nan 0.0100 -0.0002
460 0.7487 nan 0.0100 -0.0001
480 0.7445 nan 0.0100 -0.0001
500 0.7396 nan 0.0100 -0.0001
520 0.7355 nan 0.0100 -0.0001
540 0.7312 nan 0.0100 -0.0001
560 0.7270 nan 0.0100 -0.0001
580 0.7234 nan 0.0100 -0.0001
600 0.7196 nan 0.0100 -0.0001
620 0.7157 nan 0.0100 -0.0002
640 0.7121 nan 0.0100 -0.0001
660 0.7091 nan 0.0100 -0.0001
680 0.7058 nan 0.0100 -0.0001
700 0.7027 nan 0.0100 -0.0003
720 0.6999 nan 0.0100 -0.0002
740 0.6967 nan 0.0100 -0.0001
760 0.6936 nan 0.0100 -0.0002
780 0.6905 nan 0.0100 -0.0001
800 0.6872 nan 0.0100 -0.0001
820 0.6848 nan 0.0100 -0.0001
840 0.6830 nan 0.0100 -0.0003
860 0.6799 nan 0.0100 -0.0000
880 0.6774 nan 0.0100 -0.0001
900 0.6746 nan 0.0100 -0.0001
920 0.6724 nan 0.0100 -0.0001
940 0.6697 nan 0.0100 -0.0001
960 0.6669 nan 0.0100 -0.0000
980 0.6645 nan 0.0100 -0.0001
1000 0.6617 nan 0.0100 -0.0000
1020 0.6593 nan 0.0100 -0.0001
1040 0.6569 nan 0.0100 -0.0001
1060 0.6542 nan 0.0100 -0.0001
1080 0.6521 nan 0.0100 -0.0001
1100 0.6501 nan 0.0100 -0.0001
- Fold06.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2750 nan 0.1000 0.0264
2 1.2333 nan 0.1000 0.0228
3 1.1986 nan 0.1000 0.0188
4 1.1640 nan 0.1000 0.0152
5 1.1412 nan 0.1000 0.0122
6 1.1201 nan 0.1000 0.0104
7 1.0996 nan 0.1000 0.0088
8 1.0824 nan 0.1000 0.0069
9 1.0675 nan 0.1000 0.0071
10 1.0556 nan 0.1000 0.0056
20 0.9677 nan 0.1000 0.0019
40 0.8932 nan 0.1000 0.0005
60 0.8521 nan 0.1000 -0.0001
80 0.8263 nan 0.1000 -0.0013
100 0.8077 nan 0.1000 -0.0005
120 0.7944 nan 0.1000 -0.0001
140 0.7841 nan 0.1000 -0.0004
160 0.7758 nan 0.1000 -0.0006
180 0.7687 nan 0.1000 -0.0005
200 0.7623 nan 0.1000 -0.0002
220 0.7559 nan 0.1000 -0.0009
240 0.7524 nan 0.1000 -0.0010
260 0.7482 nan 0.1000 -0.0009
280 0.7431 nan 0.1000 -0.0010
300 0.7401 nan 0.1000 -0.0005
320 0.7365 nan 0.1000 -0.0006
340 0.7322 nan 0.1000 -0.0016
360 0.7293 nan 0.1000 -0.0011
380 0.7244 nan 0.1000 -0.0008
400 0.7223 nan 0.1000 -0.0009
420 0.7188 nan 0.1000 -0.0013
440 0.7151 nan 0.1000 -0.0002
460 0.7131 nan 0.1000 -0.0008
480 0.7120 nan 0.1000 -0.0011
500 0.7078 nan 0.1000 -0.0004
520 0.7046 nan 0.1000 -0.0011
540 0.7024 nan 0.1000 -0.0007
560 0.6985 nan 0.1000 -0.0006
580 0.6966 nan 0.1000 -0.0010
600 0.6940 nan 0.1000 -0.0008
620 0.6915 nan 0.1000 -0.0007
640 0.6897 nan 0.1000 -0.0005
660 0.6876 nan 0.1000 -0.0005
680 0.6859 nan 0.1000 -0.0002
700 0.6833 nan 0.1000 0.0000
720 0.6814 nan 0.1000 -0.0010
740 0.6797 nan 0.1000 -0.0013
760 0.6781 nan 0.1000 -0.0010
780 0.6754 nan 0.1000 -0.0001
800 0.6749 nan 0.1000 -0.0013
820 0.6734 nan 0.1000 -0.0008
840 0.6703 nan 0.1000 -0.0003
860 0.6690 nan 0.1000 -0.0003
880 0.6678 nan 0.1000 -0.0008
900 0.6652 nan 0.1000 -0.0010
920 0.6631 nan 0.1000 -0.0007
940 0.6613 nan 0.1000 -0.0015
960 0.6601 nan 0.1000 -0.0007
980 0.6599 nan 0.1000 -0.0009
1000 0.6586 nan 0.1000 -0.0012
1020 0.6559 nan 0.1000 -0.0005
1040 0.6551 nan 0.1000 -0.0010
1060 0.6543 nan 0.1000 -0.0009
1080 0.6530 nan 0.1000 -0.0006
1100 0.6513 nan 0.1000 -0.0002
- Fold06.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2594 nan 0.1000 0.0336
2 1.2043 nan 0.1000 0.0270
3 1.1531 nan 0.1000 0.0244
4 1.1177 nan 0.1000 0.0180
5 1.0844 nan 0.1000 0.0166
6 1.0587 nan 0.1000 0.0141
7 1.0384 nan 0.1000 0.0108
8 1.0153 nan 0.1000 0.0101
9 1.0006 nan 0.1000 0.0062
10 0.9823 nan 0.1000 0.0063
20 0.8847 nan 0.1000 0.0019
40 0.8109 nan 0.1000 -0.0003
60 0.7695 nan 0.1000 -0.0003
80 0.7415 nan 0.1000 -0.0012
100 0.7202 nan 0.1000 -0.0023
120 0.7040 nan 0.1000 -0.0009
140 0.6839 nan 0.1000 -0.0001
160 0.6687 nan 0.1000 -0.0003
180 0.6540 nan 0.1000 -0.0010
200 0.6395 nan 0.1000 -0.0008
220 0.6303 nan 0.1000 -0.0003
240 0.6223 nan 0.1000 -0.0002
260 0.6128 nan 0.1000 -0.0009
280 0.6039 nan 0.1000 -0.0009
300 0.5959 nan 0.1000 -0.0014
320 0.5869 nan 0.1000 -0.0010
340 0.5774 nan 0.1000 -0.0012
360 0.5690 nan 0.1000 -0.0008
380 0.5598 nan 0.1000 -0.0006
400 0.5530 nan 0.1000 -0.0011
420 0.5467 nan 0.1000 -0.0009
440 0.5404 nan 0.1000 -0.0008
460 0.5367 nan 0.1000 -0.0008
480 0.5300 nan 0.1000 -0.0015
500 0.5235 nan 0.1000 -0.0003
520 0.5193 nan 0.1000 -0.0026
540 0.5134 nan 0.1000 -0.0010
560 0.5070 nan 0.1000 -0.0011
580 0.5019 nan 0.1000 -0.0014
600 0.4963 nan 0.1000 -0.0003
620 0.4924 nan 0.1000 -0.0006
640 0.4886 nan 0.1000 -0.0014
660 0.4830 nan 0.1000 -0.0008
680 0.4783 nan 0.1000 -0.0010
700 0.4722 nan 0.1000 -0.0010
720 0.4671 nan 0.1000 -0.0015
740 0.4624 nan 0.1000 -0.0015
760 0.4577 nan 0.1000 -0.0009
780 0.4540 nan 0.1000 -0.0009
800 0.4506 nan 0.1000 -0.0008
820 0.4455 nan 0.1000 -0.0007
840 0.4412 nan 0.1000 -0.0007
860 0.4391 nan 0.1000 -0.0007
880 0.4347 nan 0.1000 -0.0013
900 0.4312 nan 0.1000 -0.0008
920 0.4268 nan 0.1000 -0.0011
940 0.4236 nan 0.1000 -0.0004
960 0.4203 nan 0.1000 -0.0008
980 0.4176 nan 0.1000 -0.0008
1000 0.4149 nan 0.1000 -0.0007
1020 0.4109 nan 0.1000 -0.0012
1040 0.4078 nan 0.1000 -0.0008
1060 0.4046 nan 0.1000 -0.0006
1080 0.4015 nan 0.1000 -0.0006
1100 0.3995 nan 0.1000 -0.0005
- Fold06.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2556 nan 0.1000 0.0366
2 1.1916 nan 0.1000 0.0313
3 1.1399 nan 0.1000 0.0231
4 1.0955 nan 0.1000 0.0222
5 1.0561 nan 0.1000 0.0171
6 1.0289 nan 0.1000 0.0130
7 1.0044 nan 0.1000 0.0111
8 0.9803 nan 0.1000 0.0099
9 0.9616 nan 0.1000 0.0061
10 0.9434 nan 0.1000 0.0078
20 0.8514 nan 0.1000 0.0004
40 0.7746 nan 0.1000 0.0001
60 0.7278 nan 0.1000 -0.0003
80 0.6946 nan 0.1000 -0.0017
100 0.6690 nan 0.1000 -0.0011
120 0.6412 nan 0.1000 -0.0017
140 0.6225 nan 0.1000 -0.0014
160 0.6075 nan 0.1000 -0.0016
180 0.5853 nan 0.1000 -0.0004
200 0.5643 nan 0.1000 -0.0000
220 0.5493 nan 0.1000 -0.0007
240 0.5341 nan 0.1000 -0.0010
260 0.5228 nan 0.1000 -0.0006
280 0.5110 nan 0.1000 -0.0009
300 0.5011 nan 0.1000 -0.0015
320 0.4918 nan 0.1000 -0.0016
340 0.4806 nan 0.1000 -0.0015
360 0.4719 nan 0.1000 -0.0010
380 0.4607 nan 0.1000 -0.0015
400 0.4500 nan 0.1000 -0.0012
420 0.4398 nan 0.1000 -0.0004
440 0.4330 nan 0.1000 -0.0006
460 0.4264 nan 0.1000 -0.0009
480 0.4165 nan 0.1000 -0.0013
500 0.4103 nan 0.1000 -0.0013
520 0.4021 nan 0.1000 -0.0008
540 0.3958 nan 0.1000 -0.0013
560 0.3891 nan 0.1000 -0.0019
580 0.3854 nan 0.1000 -0.0018
600 0.3795 nan 0.1000 -0.0018
620 0.3751 nan 0.1000 -0.0006
640 0.3702 nan 0.1000 -0.0005
660 0.3643 nan 0.1000 -0.0009
680 0.3587 nan 0.1000 -0.0007
700 0.3527 nan 0.1000 -0.0005
720 0.3488 nan 0.1000 -0.0011
740 0.3444 nan 0.1000 -0.0016
760 0.3419 nan 0.1000 -0.0009
780 0.3357 nan 0.1000 -0.0006
800 0.3304 nan 0.1000 -0.0005
820 0.3268 nan 0.1000 -0.0009
840 0.3220 nan 0.1000 -0.0010
860 0.3191 nan 0.1000 -0.0007
880 0.3159 nan 0.1000 -0.0008
900 0.3140 nan 0.1000 -0.0011
920 0.3105 nan 0.1000 -0.0012
940 0.3062 nan 0.1000 -0.0009
960 0.3010 nan 0.1000 -0.0011
980 0.2975 nan 0.1000 -0.0005
1000 0.2938 nan 0.1000 -0.0010
1020 0.2919 nan 0.1000 -0.0007
1040 0.2874 nan 0.1000 -0.0008
1060 0.2856 nan 0.1000 -0.0009
1080 0.2826 nan 0.1000 -0.0008
1100 0.2801 nan 0.1000 -0.0009
- Fold06.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3257 nan 0.0100 0.0030
2 1.3195 nan 0.0100 0.0029
3 1.3134 nan 0.0100 0.0027
4 1.3086 nan 0.0100 0.0027
5 1.3028 nan 0.0100 0.0028
6 1.2969 nan 0.0100 0.0027
7 1.2911 nan 0.0100 0.0027
8 1.2857 nan 0.0100 0.0026
9 1.2808 nan 0.0100 0.0026
10 1.2755 nan 0.0100 0.0025
20 1.2293 nan 0.0100 0.0019
40 1.1586 nan 0.0100 0.0014
60 1.1106 nan 0.0100 0.0011
80 1.0739 nan 0.0100 0.0008
100 1.0438 nan 0.0100 0.0006
120 1.0196 nan 0.0100 0.0005
140 0.9992 nan 0.0100 0.0004
160 0.9821 nan 0.0100 0.0003
180 0.9670 nan 0.0100 0.0002
200 0.9538 nan 0.0100 0.0002
220 0.9421 nan 0.0100 0.0001
240 0.9316 nan 0.0100 0.0002
260 0.9221 nan 0.0100 0.0002
280 0.9133 nan 0.0100 0.0001
300 0.9052 nan 0.0100 0.0002
320 0.8985 nan 0.0100 0.0000
340 0.8921 nan 0.0100 0.0001
360 0.8862 nan 0.0100 0.0001
380 0.8805 nan 0.0100 0.0000
400 0.8756 nan 0.0100 -0.0000
420 0.8708 nan 0.0100 -0.0001
440 0.8665 nan 0.0100 0.0000
460 0.8621 nan 0.0100 0.0000
480 0.8586 nan 0.0100 0.0000
500 0.8547 nan 0.0100 0.0000
520 0.8510 nan 0.0100 0.0000
540 0.8478 nan 0.0100 0.0000
560 0.8443 nan 0.0100 0.0000
580 0.8414 nan 0.0100 -0.0001
600 0.8385 nan 0.0100 -0.0000
620 0.8359 nan 0.0100 -0.0001
640 0.8331 nan 0.0100 -0.0000
660 0.8303 nan 0.0100 -0.0001
680 0.8278 nan 0.0100 0.0000
700 0.8253 nan 0.0100 0.0000
720 0.8227 nan 0.0100 -0.0000
740 0.8201 nan 0.0100 -0.0000
760 0.8180 nan 0.0100 0.0000
780 0.8157 nan 0.0100 -0.0001
800 0.8134 nan 0.0100 -0.0000
820 0.8115 nan 0.0100 -0.0001
840 0.8094 nan 0.0100 -0.0000
860 0.8074 nan 0.0100 -0.0000
880 0.8058 nan 0.0100 -0.0001
900 0.8039 nan 0.0100 0.0000
920 0.8021 nan 0.0100 -0.0000
940 0.8004 nan 0.0100 -0.0002
960 0.7988 nan 0.0100 -0.0001
980 0.7971 nan 0.0100 -0.0000
1000 0.7957 nan 0.0100 -0.0000
1020 0.7942 nan 0.0100 -0.0001
1040 0.7928 nan 0.0100 -0.0000
1060 0.7916 nan 0.0100 -0.0001
1080 0.7904 nan 0.0100 -0.0000
1100 0.7889 nan 0.0100 -0.0001
- Fold07.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3248 nan 0.0100 0.0038
2 1.3173 nan 0.0100 0.0036
3 1.3103 nan 0.0100 0.0036
4 1.3029 nan 0.0100 0.0035
5 1.2958 nan 0.0100 0.0032
6 1.2887 nan 0.0100 0.0033
7 1.2823 nan 0.0100 0.0030
8 1.2756 nan 0.0100 0.0031
9 1.2697 nan 0.0100 0.0031
10 1.2635 nan 0.0100 0.0033
20 1.2071 nan 0.0100 0.0024
40 1.1191 nan 0.0100 0.0019
60 1.0552 nan 0.0100 0.0012
80 1.0074 nan 0.0100 0.0009
100 0.9708 nan 0.0100 0.0007
120 0.9427 nan 0.0100 0.0006
140 0.9206 nan 0.0100 0.0003
160 0.9027 nan 0.0100 0.0002
180 0.8864 nan 0.0100 0.0002
200 0.8737 nan 0.0100 0.0003
220 0.8619 nan 0.0100 0.0001
240 0.8519 nan 0.0100 0.0001
260 0.8434 nan 0.0100 0.0001
280 0.8345 nan 0.0100 0.0001
300 0.8272 nan 0.0100 0.0001
320 0.8201 nan 0.0100 0.0002
340 0.8145 nan 0.0100 0.0000
360 0.8089 nan 0.0100 0.0000
380 0.8029 nan 0.0100 0.0001
400 0.7982 nan 0.0100 -0.0000
420 0.7936 nan 0.0100 0.0001
440 0.7893 nan 0.0100 -0.0001
460 0.7853 nan 0.0100 -0.0000
480 0.7816 nan 0.0100 -0.0000
500 0.7774 nan 0.0100 0.0001
520 0.7736 nan 0.0100 -0.0000
540 0.7695 nan 0.0100 -0.0000
560 0.7656 nan 0.0100 -0.0001
580 0.7624 nan 0.0100 -0.0001
600 0.7590 nan 0.0100 0.0000
620 0.7562 nan 0.0100 -0.0000
640 0.7530 nan 0.0100 -0.0001
660 0.7501 nan 0.0100 -0.0001
680 0.7473 nan 0.0100 -0.0001
700 0.7445 nan 0.0100 -0.0000
720 0.7419 nan 0.0100 0.0000
740 0.7396 nan 0.0100 -0.0001
760 0.7373 nan 0.0100 -0.0001
780 0.7351 nan 0.0100 -0.0001
800 0.7325 nan 0.0100 -0.0001
820 0.7304 nan 0.0100 -0.0001
840 0.7278 nan 0.0100 -0.0001
860 0.7256 nan 0.0100 -0.0001
880 0.7238 nan 0.0100 -0.0001
900 0.7216 nan 0.0100 -0.0000
920 0.7193 nan 0.0100 -0.0001
940 0.7171 nan 0.0100 -0.0001
960 0.7153 nan 0.0100 -0.0001
980 0.7131 nan 0.0100 -0.0001
1000 0.7108 nan 0.0100 -0.0000
1020 0.7092 nan 0.0100 -0.0001
1040 0.7074 nan 0.0100 -0.0001
1060 0.7055 nan 0.0100 -0.0002
1080 0.7035 nan 0.0100 -0.0001
1100 0.7019 nan 0.0100 -0.0001
- Fold07.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3245 nan 0.0100 0.0039
2 1.3160 nan 0.0100 0.0042
3 1.3085 nan 0.0100 0.0036
4 1.3007 nan 0.0100 0.0035
5 1.2928 nan 0.0100 0.0037
6 1.2858 nan 0.0100 0.0032
7 1.2787 nan 0.0100 0.0035
8 1.2722 nan 0.0100 0.0034
9 1.2654 nan 0.0100 0.0032
10 1.2588 nan 0.0100 0.0031
20 1.1953 nan 0.0100 0.0025
40 1.0986 nan 0.0100 0.0018
60 1.0280 nan 0.0100 0.0014
80 0.9758 nan 0.0100 0.0010
100 0.9378 nan 0.0100 0.0005
120 0.9082 nan 0.0100 0.0004
140 0.8852 nan 0.0100 0.0004
160 0.8673 nan 0.0100 0.0002
180 0.8514 nan 0.0100 0.0003
200 0.8370 nan 0.0100 0.0003
220 0.8244 nan 0.0100 0.0002
240 0.8130 nan 0.0100 -0.0000
260 0.8030 nan 0.0100 0.0001
280 0.7946 nan 0.0100 0.0000
300 0.7870 nan 0.0100 -0.0001
320 0.7800 nan 0.0100 0.0001
340 0.7730 nan 0.0100 -0.0000
360 0.7671 nan 0.0100 -0.0000
380 0.7615 nan 0.0100 0.0000
400 0.7561 nan 0.0100 -0.0000
420 0.7511 nan 0.0100 -0.0001
440 0.7464 nan 0.0100 -0.0001
460 0.7421 nan 0.0100 -0.0001
480 0.7370 nan 0.0100 -0.0001
500 0.7329 nan 0.0100 -0.0001
520 0.7284 nan 0.0100 -0.0001
540 0.7245 nan 0.0100 -0.0001
560 0.7205 nan 0.0100 -0.0001
580 0.7163 nan 0.0100 -0.0000
600 0.7123 nan 0.0100 -0.0001
620 0.7088 nan 0.0100 -0.0000
640 0.7049 nan 0.0100 -0.0001
660 0.7013 nan 0.0100 -0.0002
680 0.6976 nan 0.0100 -0.0000
700 0.6944 nan 0.0100 -0.0001
720 0.6915 nan 0.0100 -0.0001
740 0.6883 nan 0.0100 -0.0002
760 0.6849 nan 0.0100 -0.0000
780 0.6818 nan 0.0100 -0.0002
800 0.6785 nan 0.0100 -0.0000
820 0.6757 nan 0.0100 -0.0001
840 0.6731 nan 0.0100 -0.0001
860 0.6701 nan 0.0100 -0.0001
880 0.6678 nan 0.0100 -0.0001
900 0.6648 nan 0.0100 -0.0001
920 0.6620 nan 0.0100 -0.0000
940 0.6589 nan 0.0100 -0.0002
960 0.6564 nan 0.0100 -0.0001
980 0.6538 nan 0.0100 -0.0001
1000 0.6509 nan 0.0100 -0.0001
1020 0.6487 nan 0.0100 -0.0001
1040 0.6459 nan 0.0100 -0.0000
1060 0.6434 nan 0.0100 -0.0001
1080 0.6412 nan 0.0100 -0.0001
1100 0.6386 nan 0.0100 -0.0001
- Fold07.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2750 nan 0.1000 0.0286
2 1.2267 nan 0.1000 0.0236
3 1.1912 nan 0.1000 0.0189
4 1.1570 nan 0.1000 0.0158
5 1.1324 nan 0.1000 0.0133
6 1.1077 nan 0.1000 0.0110
7 1.0910 nan 0.1000 0.0090
8 1.0756 nan 0.1000 0.0076
9 1.0589 nan 0.1000 0.0069
10 1.0468 nan 0.1000 0.0062
20 0.9499 nan 0.1000 0.0020
40 0.8764 nan 0.1000 -0.0008
60 0.8380 nan 0.1000 -0.0006
80 0.8127 nan 0.1000 -0.0008
100 0.7946 nan 0.1000 -0.0008
120 0.7815 nan 0.1000 -0.0007
140 0.7700 nan 0.1000 -0.0015
160 0.7628 nan 0.1000 -0.0005
180 0.7568 nan 0.1000 -0.0008
200 0.7490 nan 0.1000 -0.0006
220 0.7438 nan 0.1000 -0.0003
240 0.7392 nan 0.1000 -0.0008
260 0.7347 nan 0.1000 -0.0009
280 0.7323 nan 0.1000 -0.0009
300 0.7277 nan 0.1000 -0.0009
320 0.7243 nan 0.1000 -0.0013
340 0.7207 nan 0.1000 -0.0008
360 0.7164 nan 0.1000 -0.0005
380 0.7130 nan 0.1000 -0.0002
400 0.7104 nan 0.1000 -0.0007
420 0.7076 nan 0.1000 -0.0007
440 0.7050 nan 0.1000 -0.0008
460 0.7019 nan 0.1000 -0.0010
480 0.7003 nan 0.1000 -0.0012
500 0.6978 nan 0.1000 -0.0006
520 0.6955 nan 0.1000 -0.0013
540 0.6934 nan 0.1000 -0.0005
560 0.6917 nan 0.1000 -0.0013
580 0.6894 nan 0.1000 -0.0009
600 0.6870 nan 0.1000 -0.0010
620 0.6850 nan 0.1000 -0.0008
640 0.6830 nan 0.1000 -0.0007
660 0.6818 nan 0.1000 -0.0006
680 0.6805 nan 0.1000 -0.0011
700 0.6777 nan 0.1000 -0.0005
720 0.6768 nan 0.1000 -0.0007
740 0.6754 nan 0.1000 -0.0014
760 0.6728 nan 0.1000 -0.0003
780 0.6718 nan 0.1000 -0.0012
800 0.6707 nan 0.1000 -0.0008
820 0.6694 nan 0.1000 -0.0003
840 0.6677 nan 0.1000 -0.0015
860 0.6643 nan 0.1000 -0.0006
880 0.6623 nan 0.1000 -0.0002
900 0.6617 nan 0.1000 -0.0003
920 0.6594 nan 0.1000 -0.0006
940 0.6577 nan 0.1000 -0.0007
960 0.6574 nan 0.1000 -0.0013
980 0.6566 nan 0.1000 -0.0014
1000 0.6554 nan 0.1000 -0.0004
1020 0.6546 nan 0.1000 -0.0005
1040 0.6519 nan 0.1000 -0.0006
1060 0.6509 nan 0.1000 -0.0005
1080 0.6498 nan 0.1000 -0.0012
1100 0.6469 nan 0.1000 -0.0014
- Fold07.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2639 nan 0.1000 0.0328
2 1.2031 nan 0.1000 0.0273
3 1.1577 nan 0.1000 0.0224
4 1.1174 nan 0.1000 0.0214
5 1.0848 nan 0.1000 0.0165
6 1.0563 nan 0.1000 0.0130
7 1.0300 nan 0.1000 0.0116
8 1.0069 nan 0.1000 0.0105
9 0.9879 nan 0.1000 0.0082
10 0.9703 nan 0.1000 0.0065
20 0.8722 nan 0.1000 0.0023
40 0.7994 nan 0.1000 -0.0013
60 0.7616 nan 0.1000 -0.0008
80 0.7372 nan 0.1000 -0.0012
100 0.7156 nan 0.1000 -0.0013
120 0.6960 nan 0.1000 -0.0013
140 0.6820 nan 0.1000 -0.0021
160 0.6721 nan 0.1000 -0.0010
180 0.6575 nan 0.1000 -0.0007
200 0.6456 nan 0.1000 -0.0010
220 0.6335 nan 0.1000 -0.0006
240 0.6216 nan 0.1000 -0.0008
260 0.6118 nan 0.1000 -0.0011
280 0.6005 nan 0.1000 -0.0015
300 0.5918 nan 0.1000 -0.0018
320 0.5827 nan 0.1000 -0.0011
340 0.5744 nan 0.1000 -0.0008
360 0.5685 nan 0.1000 -0.0009
380 0.5605 nan 0.1000 -0.0011
400 0.5521 nan 0.1000 -0.0008
420 0.5454 nan 0.1000 -0.0013
440 0.5393 nan 0.1000 -0.0008
460 0.5336 nan 0.1000 -0.0008
480 0.5284 nan 0.1000 -0.0009
500 0.5231 nan 0.1000 -0.0008
520 0.5170 nan 0.1000 -0.0005
540 0.5110 nan 0.1000 -0.0009
560 0.5052 nan 0.1000 -0.0017
580 0.5000 nan 0.1000 -0.0007
600 0.4940 nan 0.1000 -0.0009
620 0.4907 nan 0.1000 -0.0015
640 0.4841 nan 0.1000 -0.0007
660 0.4792 nan 0.1000 -0.0016
680 0.4738 nan 0.1000 -0.0007
700 0.4704 nan 0.1000 -0.0015
720 0.4660 nan 0.1000 -0.0008
740 0.4613 nan 0.1000 -0.0010
760 0.4596 nan 0.1000 -0.0011
780 0.4563 nan 0.1000 -0.0011
800 0.4521 nan 0.1000 -0.0013
820 0.4475 nan 0.1000 -0.0013
840 0.4448 nan 0.1000 -0.0015
860 0.4406 nan 0.1000 -0.0005
880 0.4373 nan 0.1000 -0.0017
900 0.4329 nan 0.1000 -0.0006
920 0.4275 nan 0.1000 -0.0009
940 0.4230 nan 0.1000 -0.0005
960 0.4198 nan 0.1000 -0.0004
980 0.4170 nan 0.1000 -0.0011
1000 0.4138 nan 0.1000 -0.0006
1020 0.4106 nan 0.1000 -0.0007
1040 0.4065 nan 0.1000 -0.0006
1060 0.4037 nan 0.1000 -0.0006
1080 0.4002 nan 0.1000 -0.0010
1100 0.3966 nan 0.1000 -0.0009
- Fold07.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2553 nan 0.1000 0.0371
2 1.1903 nan 0.1000 0.0286
3 1.1397 nan 0.1000 0.0244
4 1.0965 nan 0.1000 0.0208
5 1.0558 nan 0.1000 0.0165
6 1.0224 nan 0.1000 0.0140
7 0.9955 nan 0.1000 0.0135
8 0.9717 nan 0.1000 0.0096
9 0.9508 nan 0.1000 0.0083
10 0.9332 nan 0.1000 0.0061
20 0.8415 nan 0.1000 0.0018
40 0.7701 nan 0.1000 0.0002
60 0.7185 nan 0.1000 -0.0005
80 0.6895 nan 0.1000 -0.0011
100 0.6598 nan 0.1000 -0.0010
120 0.6380 nan 0.1000 -0.0014
140 0.6212 nan 0.1000 -0.0005
160 0.5986 nan 0.1000 -0.0018
180 0.5827 nan 0.1000 -0.0021
200 0.5700 nan 0.1000 -0.0018
220 0.5533 nan 0.1000 -0.0013
240 0.5406 nan 0.1000 -0.0009
260 0.5259 nan 0.1000 -0.0009
280 0.5133 nan 0.1000 -0.0016
300 0.5033 nan 0.1000 -0.0017
320 0.4929 nan 0.1000 -0.0007
340 0.4821 nan 0.1000 -0.0012
360 0.4732 nan 0.1000 -0.0017
380 0.4646 nan 0.1000 -0.0011
400 0.4543 nan 0.1000 -0.0007
420 0.4435 nan 0.1000 -0.0017
440 0.4329 nan 0.1000 -0.0008
460 0.4247 nan 0.1000 -0.0005
480 0.4164 nan 0.1000 -0.0009
500 0.4085 nan 0.1000 -0.0016
520 0.4028 nan 0.1000 -0.0007
540 0.3968 nan 0.1000 -0.0010
560 0.3899 nan 0.1000 -0.0007
580 0.3843 nan 0.1000 -0.0013
600 0.3785 nan 0.1000 -0.0010
620 0.3722 nan 0.1000 -0.0004
640 0.3665 nan 0.1000 -0.0005
660 0.3595 nan 0.1000 -0.0005
680 0.3552 nan 0.1000 -0.0007
700 0.3487 nan 0.1000 -0.0006
720 0.3444 nan 0.1000 -0.0009
740 0.3398 nan 0.1000 -0.0012
760 0.3362 nan 0.1000 -0.0014
780 0.3312 nan 0.1000 -0.0012
800 0.3267 nan 0.1000 -0.0004
820 0.3230 nan 0.1000 -0.0010
840 0.3190 nan 0.1000 -0.0010
860 0.3157 nan 0.1000 -0.0007
880 0.3106 nan 0.1000 -0.0009
900 0.3066 nan 0.1000 -0.0009
920 0.3009 nan 0.1000 -0.0007
940 0.2974 nan 0.1000 -0.0006
960 0.2936 nan 0.1000 -0.0004
980 0.2895 nan 0.1000 -0.0005
1000 0.2859 nan 0.1000 -0.0013
1020 0.2804 nan 0.1000 -0.0006
1040 0.2766 nan 0.1000 -0.0009
1060 0.2743 nan 0.1000 -0.0014
1080 0.2717 nan 0.1000 -0.0007
1100 0.2679 nan 0.1000 -0.0005
- Fold07.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3263 nan 0.0100 0.0028
2 1.3209 nan 0.0100 0.0028
3 1.3154 nan 0.0100 0.0028
4 1.3103 nan 0.0100 0.0027
5 1.3047 nan 0.0100 0.0027
6 1.2995 nan 0.0100 0.0026
7 1.2939 nan 0.0100 0.0026
8 1.2884 nan 0.0100 0.0025
9 1.2835 nan 0.0100 0.0025
10 1.2785 nan 0.0100 0.0024
20 1.2349 nan 0.0100 0.0020
40 1.1678 nan 0.0100 0.0015
60 1.1204 nan 0.0100 0.0009
80 1.0830 nan 0.0100 0.0008
100 1.0527 nan 0.0100 0.0007
120 1.0272 nan 0.0100 0.0005
140 1.0065 nan 0.0100 0.0004
160 0.9882 nan 0.0100 0.0003
180 0.9722 nan 0.0100 0.0003
200 0.9592 nan 0.0100 0.0002
220 0.9471 nan 0.0100 0.0002
240 0.9362 nan 0.0100 0.0002
260 0.9253 nan 0.0100 0.0002
280 0.9164 nan 0.0100 0.0001
300 0.9087 nan 0.0100 0.0000
320 0.9018 nan 0.0100 0.0001
340 0.8954 nan 0.0100 0.0002
360 0.8890 nan 0.0100 0.0000
380 0.8834 nan 0.0100 0.0001
400 0.8782 nan 0.0100 -0.0000
420 0.8733 nan 0.0100 0.0001
440 0.8685 nan 0.0100 0.0001
460 0.8638 nan 0.0100 0.0001
480 0.8594 nan 0.0100 0.0001
500 0.8555 nan 0.0100 0.0001
520 0.8517 nan 0.0100 0.0000
540 0.8481 nan 0.0100 -0.0000
560 0.8450 nan 0.0100 -0.0000
580 0.8419 nan 0.0100 -0.0000
600 0.8391 nan 0.0100 -0.0001
620 0.8360 nan 0.0100 0.0001
640 0.8333 nan 0.0100 -0.0001
660 0.8308 nan 0.0100 -0.0001
680 0.8282 nan 0.0100 -0.0001
700 0.8255 nan 0.0100 0.0000
720 0.8231 nan 0.0100 -0.0000
740 0.8207 nan 0.0100 0.0000
760 0.8183 nan 0.0100 -0.0000
780 0.8159 nan 0.0100 -0.0000
800 0.8138 nan 0.0100 -0.0001
820 0.8118 nan 0.0100 -0.0000
840 0.8100 nan 0.0100 -0.0001
860 0.8078 nan 0.0100 -0.0001
880 0.8059 nan 0.0100 0.0000
900 0.8038 nan 0.0100 -0.0000
920 0.8020 nan 0.0100 0.0000
940 0.8004 nan 0.0100 -0.0001
960 0.7990 nan 0.0100 -0.0000
980 0.7976 nan 0.0100 -0.0000
1000 0.7959 nan 0.0100 -0.0000
1020 0.7946 nan 0.0100 -0.0001
1040 0.7931 nan 0.0100 -0.0000
1060 0.7914 nan 0.0100 0.0000
1080 0.7902 nan 0.0100 -0.0001
1100 0.7887 nan 0.0100 -0.0001
- Fold08.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3237 nan 0.0100 0.0038
2 1.3163 nan 0.0100 0.0036
3 1.3090 nan 0.0100 0.0034
4 1.3022 nan 0.0100 0.0033
5 1.2949 nan 0.0100 0.0031
6 1.2882 nan 0.0100 0.0033
7 1.2815 nan 0.0100 0.0030
8 1.2747 nan 0.0100 0.0031
9 1.2685 nan 0.0100 0.0030
10 1.2621 nan 0.0100 0.0030
20 1.2067 nan 0.0100 0.0025
40 1.1192 nan 0.0100 0.0017
60 1.0554 nan 0.0100 0.0013
80 1.0069 nan 0.0100 0.0010
100 0.9696 nan 0.0100 0.0006
120 0.9414 nan 0.0100 0.0005
140 0.9185 nan 0.0100 0.0005
160 0.9001 nan 0.0100 0.0004
180 0.8840 nan 0.0100 0.0002
200 0.8692 nan 0.0100 0.0002
220 0.8572 nan 0.0100 0.0002
240 0.8466 nan 0.0100 0.0000
260 0.8376 nan 0.0100 0.0001
280 0.8294 nan 0.0100 0.0001
300 0.8215 nan 0.0100 0.0001
320 0.8146 nan 0.0100 -0.0000
340 0.8083 nan 0.0100 -0.0001
360 0.8025 nan 0.0100 -0.0000
380 0.7968 nan 0.0100 -0.0000
400 0.7918 nan 0.0100 -0.0000
420 0.7871 nan 0.0100 -0.0000
440 0.7825 nan 0.0100 -0.0001
460 0.7787 nan 0.0100 -0.0001
480 0.7748 nan 0.0100 -0.0000
500 0.7710 nan 0.0100 -0.0000
520 0.7673 nan 0.0100 0.0000
540 0.7642 nan 0.0100 -0.0001
560 0.7607 nan 0.0100 -0.0001
580 0.7571 nan 0.0100 -0.0001
600 0.7533 nan 0.0100 -0.0001
620 0.7504 nan 0.0100 -0.0000
640 0.7477 nan 0.0100 -0.0001
660 0.7450 nan 0.0100 -0.0000
680 0.7424 nan 0.0100 -0.0001
700 0.7400 nan 0.0100 -0.0000
720 0.7376 nan 0.0100 -0.0001
740 0.7350 nan 0.0100 -0.0000
760 0.7325 nan 0.0100 -0.0001
780 0.7298 nan 0.0100 -0.0001
800 0.7275 nan 0.0100 -0.0001
820 0.7252 nan 0.0100 -0.0000
840 0.7229 nan 0.0100 -0.0000
860 0.7208 nan 0.0100 -0.0001
880 0.7182 nan 0.0100 -0.0001
900 0.7163 nan 0.0100 -0.0001
920 0.7139 nan 0.0100 -0.0001
940 0.7119 nan 0.0100 -0.0001
960 0.7098 nan 0.0100 -0.0002
980 0.7081 nan 0.0100 -0.0000
1000 0.7064 nan 0.0100 -0.0001
1020 0.7041 nan 0.0100 0.0000
1040 0.7020 nan 0.0100 -0.0000
1060 0.6999 nan 0.0100 -0.0001
1080 0.6982 nan 0.0100 -0.0000
1100 0.6964 nan 0.0100 -0.0002
- Fold08.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3230 nan 0.0100 0.0042
2 1.3152 nan 0.0100 0.0039
3 1.3070 nan 0.0100 0.0036
4 1.2991 nan 0.0100 0.0038
5 1.2917 nan 0.0100 0.0037
6 1.2843 nan 0.0100 0.0034
7 1.2772 nan 0.0100 0.0036
8 1.2709 nan 0.0100 0.0031
9 1.2638 nan 0.0100 0.0034
10 1.2569 nan 0.0100 0.0035
20 1.1935 nan 0.0100 0.0028
40 1.0962 nan 0.0100 0.0020
60 1.0253 nan 0.0100 0.0015
80 0.9726 nan 0.0100 0.0011
100 0.9316 nan 0.0100 0.0006
120 0.9019 nan 0.0100 0.0003
140 0.8775 nan 0.0100 0.0004
160 0.8585 nan 0.0100 0.0003
180 0.8429 nan 0.0100 -0.0000
200 0.8284 nan 0.0100 0.0001
220 0.8166 nan 0.0100 0.0001
240 0.8057 nan 0.0100 0.0001
260 0.7961 nan 0.0100 0.0001
280 0.7872 nan 0.0100 0.0001
300 0.7800 nan 0.0100 -0.0000
320 0.7731 nan 0.0100 -0.0002
340 0.7667 nan 0.0100 0.0001
360 0.7607 nan 0.0100 0.0001
380 0.7551 nan 0.0100 -0.0001
400 0.7495 nan 0.0100 -0.0000
420 0.7442 nan 0.0100 0.0000
440 0.7395 nan 0.0100 -0.0001
460 0.7348 nan 0.0100 -0.0001
480 0.7300 nan 0.0100 -0.0002
500 0.7253 nan 0.0100 -0.0001
520 0.7204 nan 0.0100 -0.0001
540 0.7159 nan 0.0100 -0.0000
560 0.7118 nan 0.0100 -0.0000
580 0.7077 nan 0.0100 -0.0001
600 0.7039 nan 0.0100 -0.0001
620 0.6998 nan 0.0100 -0.0001
640 0.6961 nan 0.0100 -0.0001
660 0.6927 nan 0.0100 -0.0002
680 0.6888 nan 0.0100 -0.0000
700 0.6857 nan 0.0100 0.0000
720 0.6822 nan 0.0100 -0.0001
740 0.6796 nan 0.0100 -0.0001
760 0.6764 nan 0.0100 -0.0001
780 0.6734 nan 0.0100 -0.0001
800 0.6706 nan 0.0100 -0.0002
820 0.6674 nan 0.0100 -0.0001
840 0.6646 nan 0.0100 -0.0001
860 0.6619 nan 0.0100 -0.0001
880 0.6589 nan 0.0100 -0.0001
900 0.6562 nan 0.0100 0.0000
920 0.6537 nan 0.0100 -0.0001
940 0.6509 nan 0.0100 -0.0000
960 0.6483 nan 0.0100 -0.0002
980 0.6458 nan 0.0100 0.0000
1000 0.6437 nan 0.0100 -0.0001
1020 0.6411 nan 0.0100 -0.0002
1040 0.6387 nan 0.0100 -0.0001
1060 0.6364 nan 0.0100 -0.0001
1080 0.6339 nan 0.0100 -0.0001
1100 0.6318 nan 0.0100 -0.0001
- Fold08.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2717 nan 0.1000 0.0265
2 1.2279 nan 0.1000 0.0228
3 1.1927 nan 0.1000 0.0179
4 1.1635 nan 0.1000 0.0159
5 1.1423 nan 0.1000 0.0073
6 1.1176 nan 0.1000 0.0125
7 1.0978 nan 0.1000 0.0101
8 1.0810 nan 0.1000 0.0086
9 1.0664 nan 0.1000 0.0058
10 1.0528 nan 0.1000 0.0071
20 0.9547 nan 0.1000 0.0035
40 0.8788 nan 0.1000 0.0006
60 0.8395 nan 0.1000 -0.0003
80 0.8164 nan 0.1000 -0.0000
100 0.7980 nan 0.1000 -0.0004
120 0.7838 nan 0.1000 0.0001
140 0.7739 nan 0.1000 -0.0009
160 0.7656 nan 0.1000 -0.0005
180 0.7580 nan 0.1000 -0.0008
200 0.7523 nan 0.1000 -0.0006
220 0.7462 nan 0.1000 -0.0008
240 0.7403 nan 0.1000 -0.0011
260 0.7364 nan 0.1000 -0.0013
280 0.7314 nan 0.1000 -0.0004
300 0.7292 nan 0.1000 -0.0018
320 0.7242 nan 0.1000 -0.0002
340 0.7211 nan 0.1000 -0.0006
360 0.7190 nan 0.1000 -0.0006
380 0.7140 nan 0.1000 -0.0001
400 0.7100 nan 0.1000 -0.0015
420 0.7075 nan 0.1000 -0.0004
440 0.7039 nan 0.1000 -0.0006
460 0.7012 nan 0.1000 -0.0008
480 0.6983 nan 0.1000 -0.0006
500 0.6950 nan 0.1000 -0.0015
520 0.6930 nan 0.1000 -0.0012
540 0.6909 nan 0.1000 -0.0010
560 0.6876 nan 0.1000 -0.0006
580 0.6872 nan 0.1000 -0.0007
600 0.6837 nan 0.1000 -0.0010
620 0.6812 nan 0.1000 -0.0004
640 0.6788 nan 0.1000 -0.0010
660 0.6766 nan 0.1000 -0.0007
680 0.6761 nan 0.1000 -0.0003
700 0.6738 nan 0.1000 -0.0011
720 0.6716 nan 0.1000 -0.0005
740 0.6697 nan 0.1000 -0.0007
760 0.6685 nan 0.1000 -0.0014
780 0.6671 nan 0.1000 -0.0010
800 0.6650 nan 0.1000 -0.0002
820 0.6634 nan 0.1000 -0.0008
840 0.6625 nan 0.1000 -0.0002
860 0.6607 nan 0.1000 -0.0007
880 0.6585 nan 0.1000 -0.0011
900 0.6562 nan 0.1000 -0.0009
920 0.6551 nan 0.1000 -0.0008
940 0.6533 nan 0.1000 -0.0015
960 0.6516 nan 0.1000 -0.0014
980 0.6498 nan 0.1000 -0.0006
1000 0.6484 nan 0.1000 -0.0010
1020 0.6468 nan 0.1000 -0.0012
1040 0.6465 nan 0.1000 -0.0010
1060 0.6449 nan 0.1000 -0.0006
1080 0.6436 nan 0.1000 -0.0008
1100 0.6423 nan 0.1000 -0.0008
- Fold08.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2611 nan 0.1000 0.0350
2 1.2077 nan 0.1000 0.0236
3 1.1587 nan 0.1000 0.0258
4 1.1198 nan 0.1000 0.0206
5 1.0859 nan 0.1000 0.0184
6 1.0561 nan 0.1000 0.0134
7 1.0338 nan 0.1000 0.0108
8 1.0096 nan 0.1000 0.0122
9 0.9869 nan 0.1000 0.0096
10 0.9688 nan 0.1000 0.0075
20 0.8694 nan 0.1000 0.0003
40 0.7938 nan 0.1000 -0.0001
60 0.7523 nan 0.1000 -0.0006
80 0.7283 nan 0.1000 -0.0013
100 0.7078 nan 0.1000 -0.0024
120 0.6891 nan 0.1000 -0.0008
140 0.6734 nan 0.1000 -0.0013
160 0.6625 nan 0.1000 -0.0012
180 0.6525 nan 0.1000 -0.0007
200 0.6386 nan 0.1000 -0.0007
220 0.6279 nan 0.1000 -0.0012
240 0.6158 nan 0.1000 -0.0004
260 0.6029 nan 0.1000 -0.0011
280 0.5928 nan 0.1000 -0.0015
300 0.5839 nan 0.1000 -0.0008
320 0.5746 nan 0.1000 -0.0018
340 0.5668 nan 0.1000 -0.0004
360 0.5586 nan 0.1000 -0.0011
380 0.5520 nan 0.1000 -0.0009
400 0.5449 nan 0.1000 -0.0011
420 0.5366 nan 0.1000 -0.0018
440 0.5306 nan 0.1000 -0.0009
460 0.5243 nan 0.1000 -0.0007
480 0.5194 nan 0.1000 -0.0027
500 0.5133 nan 0.1000 -0.0012
520 0.5072 nan 0.1000 -0.0021
540 0.5039 nan 0.1000 -0.0014
560 0.4974 nan 0.1000 -0.0007
580 0.4937 nan 0.1000 -0.0014
600 0.4875 nan 0.1000 -0.0008
620 0.4818 nan 0.1000 -0.0013
640 0.4778 nan 0.1000 -0.0012
660 0.4714 nan 0.1000 -0.0009
680 0.4665 nan 0.1000 -0.0003
700 0.4618 nan 0.1000 -0.0010
720 0.4574 nan 0.1000 -0.0003
740 0.4544 nan 0.1000 -0.0007
760 0.4491 nan 0.1000 -0.0010
780 0.4444 nan 0.1000 -0.0004
800 0.4406 nan 0.1000 -0.0009
820 0.4377 nan 0.1000 -0.0006
840 0.4338 nan 0.1000 -0.0005
860 0.4291 nan 0.1000 -0.0006
880 0.4271 nan 0.1000 -0.0018
900 0.4235 nan 0.1000 -0.0010
920 0.4211 nan 0.1000 -0.0009
940 0.4167 nan 0.1000 -0.0003
960 0.4144 nan 0.1000 -0.0010
980 0.4113 nan 0.1000 -0.0012
1000 0.4056 nan 0.1000 -0.0003
1020 0.4011 nan 0.1000 -0.0011
1040 0.3989 nan 0.1000 -0.0007
1060 0.3951 nan 0.1000 -0.0004
1080 0.3919 nan 0.1000 -0.0005
1100 0.3884 nan 0.1000 -0.0003
- Fold08.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2543 nan 0.1000 0.0362
2 1.1909 nan 0.1000 0.0294
3 1.1409 nan 0.1000 0.0188
4 1.0918 nan 0.1000 0.0218
5 1.0557 nan 0.1000 0.0159
6 1.0207 nan 0.1000 0.0166
7 0.9916 nan 0.1000 0.0115
8 0.9686 nan 0.1000 0.0095
9 0.9456 nan 0.1000 0.0099
10 0.9298 nan 0.1000 0.0059
20 0.8246 nan 0.1000 0.0015
40 0.7446 nan 0.1000 -0.0013
60 0.7018 nan 0.1000 -0.0012
80 0.6685 nan 0.1000 -0.0011
100 0.6402 nan 0.1000 -0.0020
120 0.6193 nan 0.1000 -0.0016
140 0.6002 nan 0.1000 -0.0012
160 0.5824 nan 0.1000 -0.0011
180 0.5651 nan 0.1000 -0.0011
200 0.5527 nan 0.1000 -0.0007
220 0.5379 nan 0.1000 -0.0013
240 0.5242 nan 0.1000 -0.0010
260 0.5122 nan 0.1000 -0.0009
280 0.5005 nan 0.1000 -0.0010
300 0.4884 nan 0.1000 -0.0024
320 0.4756 nan 0.1000 -0.0011
340 0.4658 nan 0.1000 -0.0006
360 0.4584 nan 0.1000 -0.0009
380 0.4504 nan 0.1000 -0.0009
400 0.4398 nan 0.1000 -0.0016
420 0.4314 nan 0.1000 -0.0010
440 0.4210 nan 0.1000 -0.0004
460 0.4120 nan 0.1000 -0.0008
480 0.4029 nan 0.1000 -0.0012
500 0.3957 nan 0.1000 -0.0014
520 0.3891 nan 0.1000 -0.0012
540 0.3801 nan 0.1000 -0.0010
560 0.3733 nan 0.1000 -0.0013
580 0.3663 nan 0.1000 -0.0013
600 0.3603 nan 0.1000 -0.0010
620 0.3556 nan 0.1000 -0.0010
640 0.3493 nan 0.1000 -0.0014
660 0.3433 nan 0.1000 -0.0008
680 0.3374 nan 0.1000 -0.0014
700 0.3307 nan 0.1000 -0.0005
720 0.3254 nan 0.1000 -0.0010
740 0.3213 nan 0.1000 -0.0011
760 0.3160 nan 0.1000 -0.0009
780 0.3111 nan 0.1000 -0.0007
800 0.3057 nan 0.1000 -0.0006
820 0.3017 nan 0.1000 -0.0016
840 0.2954 nan 0.1000 -0.0009
860 0.2898 nan 0.1000 -0.0006
880 0.2855 nan 0.1000 -0.0005
900 0.2811 nan 0.1000 -0.0005
920 0.2773 nan 0.1000 -0.0013
940 0.2744 nan 0.1000 -0.0011
960 0.2708 nan 0.1000 -0.0007
980 0.2676 nan 0.1000 -0.0011
1000 0.2629 nan 0.1000 -0.0008
1020 0.2589 nan 0.1000 -0.0008
1040 0.2554 nan 0.1000 -0.0005
1060 0.2516 nan 0.1000 -0.0003
1080 0.2487 nan 0.1000 -0.0003
1100 0.2455 nan 0.1000 -0.0005
- Fold08.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3253 nan 0.0100 0.0030
2 1.3197 nan 0.0100 0.0028
3 1.3136 nan 0.0100 0.0028
4 1.3083 nan 0.0100 0.0028
5 1.3032 nan 0.0100 0.0026
6 1.2978 nan 0.0100 0.0026
7 1.2925 nan 0.0100 0.0027
8 1.2874 nan 0.0100 0.0026
9 1.2822 nan 0.0100 0.0027
10 1.2767 nan 0.0100 0.0025
20 1.2314 nan 0.0100 0.0021
40 1.1622 nan 0.0100 0.0014
60 1.1122 nan 0.0100 0.0008
80 1.0742 nan 0.0100 0.0007
100 1.0445 nan 0.0100 0.0006
120 1.0211 nan 0.0100 0.0006
140 1.0001 nan 0.0100 0.0004
160 0.9820 nan 0.0100 0.0001
180 0.9665 nan 0.0100 0.0003
200 0.9522 nan 0.0100 0.0002
220 0.9396 nan 0.0100 0.0003
240 0.9285 nan 0.0100 0.0003
260 0.9189 nan 0.0100 0.0003
280 0.9097 nan 0.0100 -0.0000
300 0.9021 nan 0.0100 0.0001
320 0.8948 nan 0.0100 0.0001
340 0.8881 nan 0.0100 0.0001
360 0.8817 nan 0.0100 0.0000
380 0.8761 nan 0.0100 0.0001
400 0.8710 nan 0.0100 -0.0000
420 0.8660 nan 0.0100 0.0000
440 0.8612 nan 0.0100 0.0001
460 0.8565 nan 0.0100 0.0001
480 0.8522 nan 0.0100 0.0000
500 0.8479 nan 0.0100 0.0001
520 0.8441 nan 0.0100 0.0000
540 0.8406 nan 0.0100 -0.0000
560 0.8371 nan 0.0100 0.0000
580 0.8338 nan 0.0100 0.0000
600 0.8305 nan 0.0100 -0.0000
620 0.8276 nan 0.0100 -0.0000
640 0.8249 nan 0.0100 -0.0000
660 0.8224 nan 0.0100 -0.0000
680 0.8196 nan 0.0100 -0.0000
700 0.8173 nan 0.0100 -0.0000
720 0.8150 nan 0.0100 -0.0001
740 0.8126 nan 0.0100 -0.0001
760 0.8102 nan 0.0100 -0.0000
780 0.8079 nan 0.0100 -0.0000
800 0.8061 nan 0.0100 -0.0000
820 0.8038 nan 0.0100 -0.0000
840 0.8018 nan 0.0100 -0.0000
860 0.7998 nan 0.0100 -0.0001
880 0.7978 nan 0.0100 -0.0000
900 0.7962 nan 0.0100 -0.0000
920 0.7946 nan 0.0100 -0.0000
940 0.7927 nan 0.0100 -0.0001
960 0.7910 nan 0.0100 0.0000
980 0.7893 nan 0.0100 -0.0000
1000 0.7878 nan 0.0100 -0.0000
1020 0.7866 nan 0.0100 -0.0001
1040 0.7854 nan 0.0100 -0.0000
1060 0.7838 nan 0.0100 -0.0000
1080 0.7826 nan 0.0100 -0.0000
1100 0.7812 nan 0.0100 -0.0001
- Fold09.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3241 nan 0.0100 0.0035
2 1.3173 nan 0.0100 0.0034
3 1.3101 nan 0.0100 0.0035
4 1.3036 nan 0.0100 0.0033
5 1.2964 nan 0.0100 0.0034
6 1.2894 nan 0.0100 0.0035
7 1.2825 nan 0.0100 0.0031
8 1.2759 nan 0.0100 0.0031
9 1.2700 nan 0.0100 0.0031
10 1.2636 nan 0.0100 0.0030
20 1.2065 nan 0.0100 0.0025
40 1.1163 nan 0.0100 0.0017
60 1.0512 nan 0.0100 0.0013
80 1.0029 nan 0.0100 0.0010
100 0.9651 nan 0.0100 0.0006
120 0.9356 nan 0.0100 0.0006
140 0.9127 nan 0.0100 0.0005
160 0.8936 nan 0.0100 0.0003
180 0.8784 nan 0.0100 0.0003
200 0.8650 nan 0.0100 0.0002
220 0.8529 nan 0.0100 0.0002
240 0.8428 nan 0.0100 0.0001
260 0.8338 nan 0.0100 0.0001
280 0.8255 nan 0.0100 0.0001
300 0.8182 nan 0.0100 0.0000
320 0.8115 nan 0.0100 -0.0001
340 0.8050 nan 0.0100 0.0000
360 0.7994 nan 0.0100 0.0001
380 0.7935 nan 0.0100 0.0000
400 0.7887 nan 0.0100 -0.0001
420 0.7838 nan 0.0100 0.0000
440 0.7792 nan 0.0100 -0.0001
460 0.7749 nan 0.0100 -0.0000
480 0.7714 nan 0.0100 -0.0000
500 0.7675 nan 0.0100 -0.0001
520 0.7638 nan 0.0100 -0.0000
540 0.7602 nan 0.0100 -0.0001
560 0.7569 nan 0.0100 -0.0001
580 0.7539 nan 0.0100 -0.0000
600 0.7509 nan 0.0100 -0.0002
620 0.7479 nan 0.0100 -0.0002
640 0.7447 nan 0.0100 0.0001
660 0.7418 nan 0.0100 -0.0001
680 0.7389 nan 0.0100 -0.0001
700 0.7362 nan 0.0100 0.0000
720 0.7334 nan 0.0100 -0.0001
740 0.7306 nan 0.0100 -0.0001
760 0.7279 nan 0.0100 -0.0001
780 0.7257 nan 0.0100 -0.0001
800 0.7232 nan 0.0100 -0.0001
820 0.7211 nan 0.0100 0.0000
840 0.7190 nan 0.0100 -0.0001
860 0.7167 nan 0.0100 -0.0001
880 0.7148 nan 0.0100 -0.0001
900 0.7128 nan 0.0100 -0.0001
920 0.7109 nan 0.0100 -0.0000
940 0.7087 nan 0.0100 -0.0000
960 0.7068 nan 0.0100 -0.0001
980 0.7049 nan 0.0100 -0.0001
1000 0.7028 nan 0.0100 -0.0000
1020 0.7009 nan 0.0100 -0.0000
1040 0.6992 nan 0.0100 -0.0000
1060 0.6974 nan 0.0100 -0.0001
1080 0.6956 nan 0.0100 -0.0000
1100 0.6936 nan 0.0100 -0.0001
- Fold09.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3231 nan 0.0100 0.0040
2 1.3153 nan 0.0100 0.0039
3 1.3070 nan 0.0100 0.0040
4 1.2991 nan 0.0100 0.0038
5 1.2915 nan 0.0100 0.0035
6 1.2836 nan 0.0100 0.0035
7 1.2756 nan 0.0100 0.0034
8 1.2686 nan 0.0100 0.0036
9 1.2618 nan 0.0100 0.0031
10 1.2547 nan 0.0100 0.0032
20 1.1930 nan 0.0100 0.0028
40 1.0951 nan 0.0100 0.0019
60 1.0257 nan 0.0100 0.0014
80 0.9739 nan 0.0100 0.0011
100 0.9331 nan 0.0100 0.0007
120 0.9018 nan 0.0100 0.0006
140 0.8781 nan 0.0100 0.0003
160 0.8588 nan 0.0100 0.0002
180 0.8433 nan 0.0100 0.0002
200 0.8297 nan 0.0100 0.0001
220 0.8170 nan 0.0100 0.0001
240 0.8066 nan 0.0100 0.0001
260 0.7963 nan 0.0100 0.0001
280 0.7879 nan 0.0100 0.0001
300 0.7807 nan 0.0100 0.0000
320 0.7735 nan 0.0100 -0.0000
340 0.7662 nan 0.0100 0.0000
360 0.7595 nan 0.0100 -0.0000
380 0.7537 nan 0.0100 0.0000
400 0.7478 nan 0.0100 -0.0000
420 0.7427 nan 0.0100 0.0000
440 0.7380 nan 0.0100 -0.0000
460 0.7329 nan 0.0100 -0.0001
480 0.7285 nan 0.0100 -0.0001
500 0.7241 nan 0.0100 -0.0000
520 0.7197 nan 0.0100 -0.0000
540 0.7154 nan 0.0100 -0.0001
560 0.7111 nan 0.0100 -0.0002
580 0.7073 nan 0.0100 -0.0001
600 0.7035 nan 0.0100 -0.0001
620 0.6994 nan 0.0100 -0.0001
640 0.6951 nan 0.0100 -0.0001
660 0.6914 nan 0.0100 -0.0002
680 0.6880 nan 0.0100 -0.0000
700 0.6852 nan 0.0100 -0.0002
720 0.6814 nan 0.0100 -0.0001
740 0.6782 nan 0.0100 -0.0001
760 0.6752 nan 0.0100 -0.0001
780 0.6720 nan 0.0100 -0.0001
800 0.6681 nan 0.0100 -0.0001
820 0.6651 nan 0.0100 -0.0002
840 0.6620 nan 0.0100 -0.0002
860 0.6593 nan 0.0100 -0.0003
880 0.6567 nan 0.0100 -0.0000
900 0.6535 nan 0.0100 -0.0001
920 0.6509 nan 0.0100 0.0000
940 0.6484 nan 0.0100 -0.0001
960 0.6457 nan 0.0100 -0.0001
980 0.6429 nan 0.0100 -0.0001
1000 0.6405 nan 0.0100 -0.0001
1020 0.6382 nan 0.0100 -0.0002
1040 0.6361 nan 0.0100 -0.0002
1060 0.6333 nan 0.0100 -0.0001
1080 0.6313 nan 0.0100 -0.0001
1100 0.6285 nan 0.0100 -0.0001
- Fold09.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2696 nan 0.1000 0.0285
2 1.2226 nan 0.1000 0.0218
3 1.1837 nan 0.1000 0.0181
4 1.1503 nan 0.1000 0.0153
5 1.1228 nan 0.1000 0.0119
6 1.1015 nan 0.1000 0.0096
7 1.0840 nan 0.1000 0.0069
8 1.0656 nan 0.1000 0.0082
9 1.0511 nan 0.1000 0.0069
10 1.0367 nan 0.1000 0.0063
20 0.9457 nan 0.1000 0.0019
40 0.8640 nan 0.1000 0.0010
60 0.8276 nan 0.1000 -0.0000
80 0.8032 nan 0.1000 -0.0006
100 0.7880 nan 0.1000 -0.0001
120 0.7755 nan 0.1000 -0.0007
140 0.7661 nan 0.1000 -0.0009
160 0.7592 nan 0.1000 -0.0007
180 0.7515 nan 0.1000 -0.0015
200 0.7438 nan 0.1000 -0.0003
220 0.7381 nan 0.1000 -0.0003
240 0.7325 nan 0.1000 -0.0004
260 0.7280 nan 0.1000 -0.0012
280 0.7232 nan 0.1000 -0.0014
300 0.7188 nan 0.1000 -0.0006
320 0.7145 nan 0.1000 -0.0004
340 0.7110 nan 0.1000 -0.0007
360 0.7081 nan 0.1000 -0.0010
380 0.7042 nan 0.1000 -0.0010
400 0.6999 nan 0.1000 -0.0002
420 0.6976 nan 0.1000 -0.0011
440 0.6955 nan 0.1000 -0.0007
460 0.6922 nan 0.1000 -0.0001
480 0.6905 nan 0.1000 -0.0005
500 0.6886 nan 0.1000 -0.0005
520 0.6867 nan 0.1000 -0.0009
540 0.6843 nan 0.1000 -0.0005
560 0.6815 nan 0.1000 -0.0006
580 0.6799 nan 0.1000 -0.0006
600 0.6786 nan 0.1000 -0.0009
620 0.6772 nan 0.1000 -0.0003
640 0.6761 nan 0.1000 -0.0008
660 0.6734 nan 0.1000 -0.0005
680 0.6708 nan 0.1000 -0.0003
700 0.6685 nan 0.1000 -0.0010
720 0.6675 nan 0.1000 -0.0006
740 0.6673 nan 0.1000 -0.0007
760 0.6652 nan 0.1000 -0.0006
780 0.6634 nan 0.1000 -0.0008
800 0.6615 nan 0.1000 -0.0005
820 0.6596 nan 0.1000 -0.0003
840 0.6583 nan 0.1000 -0.0006
860 0.6564 nan 0.1000 -0.0004
880 0.6544 nan 0.1000 -0.0012
900 0.6523 nan 0.1000 -0.0014
920 0.6511 nan 0.1000 -0.0009
940 0.6490 nan 0.1000 -0.0007
960 0.6470 nan 0.1000 -0.0007
980 0.6463 nan 0.1000 -0.0006
1000 0.6445 nan 0.1000 -0.0011
1020 0.6430 nan 0.1000 -0.0010
1040 0.6406 nan 0.1000 -0.0011
1060 0.6405 nan 0.1000 -0.0010
1080 0.6395 nan 0.1000 -0.0010
1100 0.6383 nan 0.1000 -0.0008
- Fold09.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2580 nan 0.1000 0.0372
2 1.1998 nan 0.1000 0.0264
3 1.1530 nan 0.1000 0.0232
4 1.1130 nan 0.1000 0.0208
5 1.0736 nan 0.1000 0.0178
6 1.0433 nan 0.1000 0.0147
7 1.0167 nan 0.1000 0.0100
8 0.9983 nan 0.1000 0.0084
9 0.9785 nan 0.1000 0.0091
10 0.9645 nan 0.1000 0.0061
20 0.8633 nan 0.1000 0.0023
40 0.7859 nan 0.1000 0.0002
60 0.7528 nan 0.1000 -0.0007
80 0.7210 nan 0.1000 -0.0002
100 0.6977 nan 0.1000 -0.0012
120 0.6795 nan 0.1000 -0.0012
140 0.6640 nan 0.1000 -0.0007
160 0.6469 nan 0.1000 -0.0006
180 0.6309 nan 0.1000 -0.0011
200 0.6163 nan 0.1000 -0.0009
220 0.6095 nan 0.1000 -0.0007
240 0.5999 nan 0.1000 -0.0007
260 0.5916 nan 0.1000 -0.0009
280 0.5838 nan 0.1000 -0.0015
300 0.5757 nan 0.1000 -0.0004
320 0.5655 nan 0.1000 -0.0011
340 0.5599 nan 0.1000 -0.0014
360 0.5540 nan 0.1000 -0.0011
380 0.5474 nan 0.1000 -0.0013
400 0.5400 nan 0.1000 -0.0009
420 0.5342 nan 0.1000 -0.0009
440 0.5292 nan 0.1000 -0.0014
460 0.5240 nan 0.1000 -0.0010
480 0.5187 nan 0.1000 -0.0015
500 0.5120 nan 0.1000 -0.0003
520 0.5078 nan 0.1000 -0.0005
540 0.5000 nan 0.1000 -0.0009
560 0.4946 nan 0.1000 -0.0017
580 0.4891 nan 0.1000 -0.0009
600 0.4830 nan 0.1000 -0.0008
620 0.4760 nan 0.1000 -0.0008
640 0.4727 nan 0.1000 -0.0007
660 0.4689 nan 0.1000 -0.0012
680 0.4650 nan 0.1000 -0.0003
700 0.4602 nan 0.1000 -0.0010
720 0.4562 nan 0.1000 -0.0011
740 0.4515 nan 0.1000 -0.0008
760 0.4472 nan 0.1000 -0.0012
780 0.4437 nan 0.1000 -0.0008
800 0.4405 nan 0.1000 -0.0004
820 0.4368 nan 0.1000 -0.0007
840 0.4325 nan 0.1000 -0.0007
860 0.4302 nan 0.1000 -0.0014
880 0.4269 nan 0.1000 -0.0002
900 0.4225 nan 0.1000 -0.0012
920 0.4190 nan 0.1000 -0.0007
940 0.4159 nan 0.1000 -0.0011
960 0.4130 nan 0.1000 -0.0013
980 0.4090 nan 0.1000 -0.0011
1000 0.4042 nan 0.1000 -0.0007
1020 0.4008 nan 0.1000 -0.0005
1040 0.3974 nan 0.1000 -0.0005
1060 0.3939 nan 0.1000 -0.0009
1080 0.3918 nan 0.1000 -0.0012
1100 0.3884 nan 0.1000 -0.0004
- Fold09.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2566 nan 0.1000 0.0366
2 1.1935 nan 0.1000 0.0312
3 1.1366 nan 0.1000 0.0255
4 1.0906 nan 0.1000 0.0207
5 1.0526 nan 0.1000 0.0177
6 1.0216 nan 0.1000 0.0160
7 0.9932 nan 0.1000 0.0114
8 0.9667 nan 0.1000 0.0118
9 0.9425 nan 0.1000 0.0113
10 0.9236 nan 0.1000 0.0094
20 0.8303 nan 0.1000 0.0011
40 0.7456 nan 0.1000 0.0006
60 0.6977 nan 0.1000 -0.0004
80 0.6617 nan 0.1000 -0.0008
100 0.6373 nan 0.1000 -0.0007
120 0.6148 nan 0.1000 -0.0006
140 0.5921 nan 0.1000 -0.0007
160 0.5720 nan 0.1000 -0.0011
180 0.5545 nan 0.1000 -0.0006
200 0.5411 nan 0.1000 -0.0010
220 0.5281 nan 0.1000 -0.0006
240 0.5168 nan 0.1000 -0.0014
260 0.5056 nan 0.1000 -0.0013
280 0.4942 nan 0.1000 -0.0011
300 0.4833 nan 0.1000 -0.0017
320 0.4700 nan 0.1000 -0.0011
340 0.4627 nan 0.1000 -0.0017
360 0.4511 nan 0.1000 -0.0011
380 0.4392 nan 0.1000 -0.0006
400 0.4318 nan 0.1000 -0.0011
420 0.4231 nan 0.1000 -0.0016
440 0.4145 nan 0.1000 -0.0010
460 0.4091 nan 0.1000 -0.0008
480 0.4005 nan 0.1000 -0.0011
500 0.3905 nan 0.1000 -0.0014
520 0.3830 nan 0.1000 -0.0007
540 0.3746 nan 0.1000 -0.0012
560 0.3681 nan 0.1000 -0.0004
580 0.3627 nan 0.1000 -0.0022
600 0.3573 nan 0.1000 -0.0005
620 0.3515 nan 0.1000 -0.0008
640 0.3466 nan 0.1000 -0.0007
660 0.3404 nan 0.1000 -0.0010
680 0.3354 nan 0.1000 -0.0014
700 0.3292 nan 0.1000 -0.0012
720 0.3231 nan 0.1000 -0.0009
740 0.3187 nan 0.1000 -0.0016
760 0.3131 nan 0.1000 -0.0014
780 0.3065 nan 0.1000 -0.0008
800 0.3005 nan 0.1000 -0.0008
820 0.2972 nan 0.1000 -0.0008
840 0.2911 nan 0.1000 -0.0006
860 0.2865 nan 0.1000 -0.0008
880 0.2822 nan 0.1000 -0.0005
900 0.2783 nan 0.1000 -0.0006
920 0.2739 nan 0.1000 -0.0011
940 0.2703 nan 0.1000 -0.0012
960 0.2677 nan 0.1000 -0.0004
980 0.2652 nan 0.1000 -0.0009
1000 0.2626 nan 0.1000 -0.0014
1020 0.2599 nan 0.1000 -0.0014
1040 0.2571 nan 0.1000 -0.0012
1060 0.2540 nan 0.1000 -0.0010
1080 0.2516 nan 0.1000 -0.0004
1100 0.2497 nan 0.1000 -0.0007
- Fold09.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3254 nan 0.0100 0.0031
2 1.3190 nan 0.0100 0.0030
3 1.3130 nan 0.0100 0.0032
4 1.3071 nan 0.0100 0.0030
5 1.3016 nan 0.0100 0.0028
6 1.2954 nan 0.0100 0.0029
7 1.2895 nan 0.0100 0.0027
8 1.2838 nan 0.0100 0.0027
9 1.2781 nan 0.0100 0.0026
10 1.2730 nan 0.0100 0.0026
20 1.2264 nan 0.0100 0.0021
40 1.1554 nan 0.0100 0.0015
60 1.1042 nan 0.0100 0.0011
80 1.0670 nan 0.0100 0.0007
100 1.0364 nan 0.0100 0.0006
120 1.0118 nan 0.0100 0.0005
140 0.9913 nan 0.0100 0.0003
160 0.9740 nan 0.0100 0.0003
180 0.9590 nan 0.0100 0.0003
200 0.9463 nan 0.0100 0.0002
220 0.9347 nan 0.0100 0.0002
240 0.9248 nan 0.0100 0.0001
260 0.9152 nan 0.0100 0.0001
280 0.9070 nan 0.0100 0.0001
300 0.8998 nan 0.0100 0.0001
320 0.8929 nan 0.0100 0.0001
340 0.8872 nan 0.0100 0.0000
360 0.8812 nan 0.0100 0.0000
380 0.8758 nan 0.0100 0.0000
400 0.8713 nan 0.0100 0.0001
420 0.8668 nan 0.0100 0.0000
440 0.8623 nan 0.0100 0.0001
460 0.8582 nan 0.0100 0.0000
480 0.8546 nan 0.0100 -0.0001
500 0.8508 nan 0.0100 -0.0000
520 0.8472 nan 0.0100 -0.0000
540 0.8437 nan 0.0100 -0.0000
560 0.8406 nan 0.0100 -0.0000
580 0.8375 nan 0.0100 0.0000
600 0.8345 nan 0.0100 0.0000
620 0.8318 nan 0.0100 -0.0000
640 0.8295 nan 0.0100 0.0000
660 0.8268 nan 0.0100 0.0000
680 0.8243 nan 0.0100 -0.0001
700 0.8220 nan 0.0100 0.0000
720 0.8196 nan 0.0100 -0.0000
740 0.8173 nan 0.0100 -0.0001
760 0.8151 nan 0.0100 -0.0000
780 0.8130 nan 0.0100 0.0000
800 0.8108 nan 0.0100 -0.0001
820 0.8090 nan 0.0100 -0.0000
840 0.8070 nan 0.0100 -0.0000
860 0.8054 nan 0.0100 -0.0000
880 0.8036 nan 0.0100 -0.0000
900 0.8020 nan 0.0100 -0.0000
920 0.8001 nan 0.0100 -0.0001
940 0.7983 nan 0.0100 -0.0000
960 0.7968 nan 0.0100 -0.0001
980 0.7954 nan 0.0100 -0.0000
1000 0.7937 nan 0.0100 -0.0000
1020 0.7922 nan 0.0100 -0.0000
1040 0.7907 nan 0.0100 -0.0001
1060 0.7894 nan 0.0100 -0.0001
1080 0.7880 nan 0.0100 -0.0000
1100 0.7867 nan 0.0100 -0.0000
- Fold10.Rep1: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0038
2 1.3169 nan 0.0100 0.0037
3 1.3092 nan 0.0100 0.0036
4 1.3023 nan 0.0100 0.0033
5 1.2955 nan 0.0100 0.0034
6 1.2885 nan 0.0100 0.0034
7 1.2817 nan 0.0100 0.0036
8 1.2752 nan 0.0100 0.0032
9 1.2683 nan 0.0100 0.0030
10 1.2621 nan 0.0100 0.0032
20 1.2041 nan 0.0100 0.0025
40 1.1145 nan 0.0100 0.0017
60 1.0504 nan 0.0100 0.0011
80 1.0007 nan 0.0100 0.0010
100 0.9622 nan 0.0100 0.0007
120 0.9329 nan 0.0100 0.0003
140 0.9104 nan 0.0100 0.0003
160 0.8925 nan 0.0100 0.0002
180 0.8785 nan 0.0100 0.0000
200 0.8660 nan 0.0100 0.0000
220 0.8551 nan 0.0100 0.0001
240 0.8449 nan 0.0100 0.0001
260 0.8362 nan 0.0100 0.0001
280 0.8286 nan 0.0100 0.0000
300 0.8204 nan 0.0100 0.0001
320 0.8138 nan 0.0100 0.0000
340 0.8076 nan 0.0100 -0.0000
360 0.8018 nan 0.0100 0.0001
380 0.7972 nan 0.0100 0.0000
400 0.7926 nan 0.0100 -0.0000
420 0.7882 nan 0.0100 -0.0000
440 0.7840 nan 0.0100 -0.0001
460 0.7798 nan 0.0100 0.0000
480 0.7758 nan 0.0100 0.0000
500 0.7718 nan 0.0100 -0.0000
520 0.7674 nan 0.0100 0.0000
540 0.7639 nan 0.0100 -0.0001
560 0.7607 nan 0.0100 -0.0001
580 0.7575 nan 0.0100 0.0000
600 0.7546 nan 0.0100 -0.0000
620 0.7515 nan 0.0100 -0.0001
640 0.7488 nan 0.0100 -0.0001
660 0.7460 nan 0.0100 -0.0001
680 0.7431 nan 0.0100 -0.0000
700 0.7401 nan 0.0100 -0.0001
720 0.7378 nan 0.0100 -0.0001
740 0.7350 nan 0.0100 -0.0000
760 0.7322 nan 0.0100 -0.0002
780 0.7295 nan 0.0100 -0.0001
800 0.7269 nan 0.0100 -0.0000
820 0.7247 nan 0.0100 -0.0001
840 0.7224 nan 0.0100 -0.0001
860 0.7202 nan 0.0100 -0.0000
880 0.7181 nan 0.0100 -0.0001
900 0.7160 nan 0.0100 -0.0001
920 0.7134 nan 0.0100 -0.0001
940 0.7110 nan 0.0100 -0.0001
960 0.7092 nan 0.0100 -0.0002
980 0.7066 nan 0.0100 -0.0001
1000 0.7047 nan 0.0100 -0.0001
1020 0.7027 nan 0.0100 -0.0000
1040 0.7010 nan 0.0100 -0.0001
1060 0.6990 nan 0.0100 -0.0000
1080 0.6972 nan 0.0100 -0.0000
1100 0.6952 nan 0.0100 -0.0001
- Fold10.Rep1: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3233 nan 0.0100 0.0041
2 1.3148 nan 0.0100 0.0038
3 1.3070 nan 0.0100 0.0036
4 1.2989 nan 0.0100 0.0037
5 1.2912 nan 0.0100 0.0040
6 1.2835 nan 0.0100 0.0038
7 1.2760 nan 0.0100 0.0035
8 1.2685 nan 0.0100 0.0035
9 1.2615 nan 0.0100 0.0034
10 1.2547 nan 0.0100 0.0032
20 1.1915 nan 0.0100 0.0030
40 1.0950 nan 0.0100 0.0020
60 1.0245 nan 0.0100 0.0015
80 0.9721 nan 0.0100 0.0011
100 0.9328 nan 0.0100 0.0009
120 0.9032 nan 0.0100 0.0006
140 0.8790 nan 0.0100 0.0004
160 0.8594 nan 0.0100 0.0001
180 0.8426 nan 0.0100 0.0003
200 0.8287 nan 0.0100 0.0001
220 0.8168 nan 0.0100 0.0001
240 0.8075 nan 0.0100 0.0001
260 0.7980 nan 0.0100 0.0001
280 0.7894 nan 0.0100 0.0001
300 0.7813 nan 0.0100 0.0000
320 0.7741 nan 0.0100 0.0001
340 0.7669 nan 0.0100 -0.0000
360 0.7605 nan 0.0100 -0.0000
380 0.7544 nan 0.0100 0.0001
400 0.7494 nan 0.0100 -0.0002
420 0.7444 nan 0.0100 -0.0001
440 0.7396 nan 0.0100 -0.0000
460 0.7349 nan 0.0100 -0.0001
480 0.7300 nan 0.0100 -0.0001
500 0.7252 nan 0.0100 -0.0001
520 0.7208 nan 0.0100 -0.0001
540 0.7171 nan 0.0100 -0.0001
560 0.7134 nan 0.0100 -0.0002
580 0.7094 nan 0.0100 -0.0001
600 0.7057 nan 0.0100 -0.0001
620 0.7021 nan 0.0100 -0.0001
640 0.6985 nan 0.0100 -0.0001
660 0.6959 nan 0.0100 -0.0001
680 0.6928 nan 0.0100 -0.0001
700 0.6894 nan 0.0100 -0.0002
720 0.6863 nan 0.0100 -0.0001
740 0.6829 nan 0.0100 -0.0002
760 0.6799 nan 0.0100 -0.0001
780 0.6774 nan 0.0100 -0.0000
800 0.6744 nan 0.0100 -0.0001
820 0.6713 nan 0.0100 -0.0002
840 0.6687 nan 0.0100 -0.0001
860 0.6659 nan 0.0100 -0.0001
880 0.6633 nan 0.0100 -0.0002
900 0.6603 nan 0.0100 -0.0001
920 0.6576 nan 0.0100 -0.0000
940 0.6547 nan 0.0100 -0.0001
960 0.6518 nan 0.0100 -0.0001
980 0.6495 nan 0.0100 -0.0001
1000 0.6477 nan 0.0100 -0.0001
1020 0.6453 nan 0.0100 -0.0001
1040 0.6430 nan 0.0100 -0.0002
1060 0.6406 nan 0.0100 -0.0001
1080 0.6383 nan 0.0100 -0.0001
1100 0.6357 nan 0.0100 -0.0001
- Fold10.Rep1: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2763 nan 0.1000 0.0297
2 1.2289 nan 0.1000 0.0251
3 1.1877 nan 0.1000 0.0203
4 1.1511 nan 0.1000 0.0163
5 1.1208 nan 0.1000 0.0133
6 1.0997 nan 0.1000 0.0111
7 1.0819 nan 0.1000 0.0066
8 1.0674 nan 0.1000 0.0065
9 1.0477 nan 0.1000 0.0085
10 1.0331 nan 0.1000 0.0072
20 0.9397 nan 0.1000 0.0020
40 0.8680 nan 0.1000 0.0010
60 0.8332 nan 0.1000 -0.0007
80 0.8123 nan 0.1000 -0.0009
100 0.7940 nan 0.1000 0.0001
120 0.7795 nan 0.1000 -0.0007
140 0.7670 nan 0.1000 -0.0007
160 0.7586 nan 0.1000 -0.0011
180 0.7516 nan 0.1000 -0.0010
200 0.7438 nan 0.1000 -0.0000
220 0.7383 nan 0.1000 -0.0005
240 0.7339 nan 0.1000 -0.0009
260 0.7295 nan 0.1000 -0.0011
280 0.7252 nan 0.1000 -0.0007
300 0.7213 nan 0.1000 -0.0016
320 0.7173 nan 0.1000 -0.0007
340 0.7127 nan 0.1000 -0.0007
360 0.7095 nan 0.1000 -0.0006
380 0.7067 nan 0.1000 -0.0006
400 0.7036 nan 0.1000 -0.0006
420 0.7000 nan 0.1000 -0.0007
440 0.6974 nan 0.1000 -0.0007
460 0.6930 nan 0.1000 -0.0009
480 0.6916 nan 0.1000 -0.0006
500 0.6889 nan 0.1000 -0.0010
520 0.6852 nan 0.1000 -0.0003
540 0.6825 nan 0.1000 -0.0007
560 0.6812 nan 0.1000 -0.0015
580 0.6782 nan 0.1000 -0.0007
600 0.6749 nan 0.1000 -0.0011
620 0.6731 nan 0.1000 -0.0003
640 0.6698 nan 0.1000 -0.0013
660 0.6679 nan 0.1000 -0.0005
680 0.6668 nan 0.1000 -0.0012
700 0.6660 nan 0.1000 -0.0008
720 0.6634 nan 0.1000 -0.0005
740 0.6613 nan 0.1000 -0.0008
760 0.6597 nan 0.1000 -0.0012
780 0.6582 nan 0.1000 -0.0002
800 0.6561 nan 0.1000 -0.0018
820 0.6548 nan 0.1000 -0.0007
840 0.6533 nan 0.1000 -0.0005
860 0.6510 nan 0.1000 -0.0005
880 0.6494 nan 0.1000 -0.0004
900 0.6483 nan 0.1000 -0.0007
920 0.6486 nan 0.1000 -0.0012
940 0.6461 nan 0.1000 -0.0006
960 0.6441 nan 0.1000 -0.0012
980 0.6418 nan 0.1000 -0.0008
1000 0.6395 nan 0.1000 -0.0003
1020 0.6367 nan 0.1000 -0.0005
1040 0.6352 nan 0.1000 -0.0002
1060 0.6344 nan 0.1000 -0.0004
1080 0.6332 nan 0.1000 -0.0013
1100 0.6316 nan 0.1000 -0.0013
- Fold10.Rep1: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2651 nan 0.1000 0.0386
2 1.2053 nan 0.1000 0.0307
3 1.1551 nan 0.1000 0.0250
4 1.1105 nan 0.1000 0.0197
5 1.0745 nan 0.1000 0.0173
6 1.0458 nan 0.1000 0.0130
7 1.0203 nan 0.1000 0.0114
8 0.9973 nan 0.1000 0.0116
9 0.9773 nan 0.1000 0.0080
10 0.9620 nan 0.1000 0.0072
20 0.8612 nan 0.1000 0.0007
40 0.7926 nan 0.1000 -0.0000
60 0.7575 nan 0.1000 -0.0002
80 0.7311 nan 0.1000 -0.0004
100 0.7087 nan 0.1000 0.0003
120 0.6902 nan 0.1000 -0.0015
140 0.6767 nan 0.1000 -0.0014
160 0.6605 nan 0.1000 -0.0009
180 0.6460 nan 0.1000 -0.0008
200 0.6338 nan 0.1000 -0.0004
220 0.6231 nan 0.1000 -0.0014
240 0.6121 nan 0.1000 -0.0008
260 0.6036 nan 0.1000 -0.0004
280 0.5923 nan 0.1000 -0.0015
300 0.5798 nan 0.1000 -0.0007
320 0.5717 nan 0.1000 -0.0009
340 0.5636 nan 0.1000 -0.0008
360 0.5546 nan 0.1000 -0.0006
380 0.5499 nan 0.1000 -0.0010
400 0.5438 nan 0.1000 -0.0009
420 0.5383 nan 0.1000 -0.0017
440 0.5314 nan 0.1000 -0.0009
460 0.5233 nan 0.1000 -0.0010
480 0.5156 nan 0.1000 -0.0009
500 0.5092 nan 0.1000 -0.0013
520 0.5014 nan 0.1000 -0.0014
540 0.4963 nan 0.1000 -0.0010
560 0.4904 nan 0.1000 -0.0010
580 0.4849 nan 0.1000 -0.0008
600 0.4790 nan 0.1000 -0.0012
620 0.4755 nan 0.1000 -0.0008
640 0.4702 nan 0.1000 -0.0012
660 0.4651 nan 0.1000 -0.0010
680 0.4580 nan 0.1000 -0.0010
700 0.4534 nan 0.1000 -0.0006
720 0.4488 nan 0.1000 -0.0007
740 0.4452 nan 0.1000 -0.0006
760 0.4415 nan 0.1000 -0.0006
780 0.4382 nan 0.1000 -0.0015
800 0.4334 nan 0.1000 -0.0008
820 0.4293 nan 0.1000 -0.0005
840 0.4263 nan 0.1000 -0.0008
860 0.4230 nan 0.1000 -0.0016
880 0.4208 nan 0.1000 -0.0006
900 0.4170 nan 0.1000 -0.0009
920 0.4122 nan 0.1000 -0.0008
940 0.4089 nan 0.1000 -0.0008
960 0.4059 nan 0.1000 -0.0005
980 0.4023 nan 0.1000 -0.0007
1000 0.3974 nan 0.1000 -0.0007
1020 0.3931 nan 0.1000 -0.0006
1040 0.3895 nan 0.1000 -0.0007
1060 0.3864 nan 0.1000 -0.0005
1080 0.3838 nan 0.1000 -0.0012
1100 0.3814 nan 0.1000 -0.0009
- Fold10.Rep1: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2520 nan 0.1000 0.0367
2 1.1895 nan 0.1000 0.0327
3 1.1401 nan 0.1000 0.0237
4 1.0953 nan 0.1000 0.0204
5 1.0594 nan 0.1000 0.0155
6 1.0278 nan 0.1000 0.0155
7 0.9948 nan 0.1000 0.0149
8 0.9699 nan 0.1000 0.0126
9 0.9472 nan 0.1000 0.0100
10 0.9285 nan 0.1000 0.0073
20 0.8250 nan 0.1000 0.0016
40 0.7467 nan 0.1000 -0.0003
60 0.7058 nan 0.1000 -0.0009
80 0.6704 nan 0.1000 -0.0012
100 0.6415 nan 0.1000 -0.0013
120 0.6197 nan 0.1000 -0.0014
140 0.6027 nan 0.1000 -0.0012
160 0.5863 nan 0.1000 -0.0008
180 0.5699 nan 0.1000 -0.0010
200 0.5530 nan 0.1000 -0.0012
220 0.5371 nan 0.1000 -0.0008
240 0.5199 nan 0.1000 -0.0002
260 0.5070 nan 0.1000 -0.0018
280 0.4974 nan 0.1000 -0.0012
300 0.4835 nan 0.1000 -0.0008
320 0.4740 nan 0.1000 -0.0007
340 0.4661 nan 0.1000 -0.0011
360 0.4557 nan 0.1000 -0.0011
380 0.4476 nan 0.1000 -0.0007
400 0.4371 nan 0.1000 -0.0005
420 0.4314 nan 0.1000 -0.0006
440 0.4213 nan 0.1000 -0.0019
460 0.4134 nan 0.1000 -0.0009
480 0.4051 nan 0.1000 -0.0005
500 0.4001 nan 0.1000 -0.0007
520 0.3941 nan 0.1000 -0.0009
540 0.3879 nan 0.1000 -0.0006
560 0.3809 nan 0.1000 -0.0015
580 0.3725 nan 0.1000 -0.0006
600 0.3685 nan 0.1000 -0.0010
620 0.3608 nan 0.1000 -0.0016
640 0.3533 nan 0.1000 -0.0007
660 0.3480 nan 0.1000 -0.0006
680 0.3428 nan 0.1000 -0.0009
700 0.3375 nan 0.1000 -0.0005
720 0.3336 nan 0.1000 -0.0006
740 0.3288 nan 0.1000 -0.0009
760 0.3250 nan 0.1000 -0.0004
780 0.3203 nan 0.1000 -0.0011
800 0.3145 nan 0.1000 -0.0006
820 0.3106 nan 0.1000 -0.0004
840 0.3070 nan 0.1000 -0.0012
860 0.3029 nan 0.1000 -0.0008
880 0.2995 nan 0.1000 -0.0006
900 0.2962 nan 0.1000 -0.0012
920 0.2921 nan 0.1000 -0.0009
940 0.2874 nan 0.1000 -0.0011
960 0.2830 nan 0.1000 -0.0007
980 0.2789 nan 0.1000 -0.0006
1000 0.2757 nan 0.1000 -0.0002
1020 0.2732 nan 0.1000 -0.0015
1040 0.2687 nan 0.1000 -0.0004
1060 0.2660 nan 0.1000 -0.0007
1080 0.2630 nan 0.1000 -0.0007
1100 0.2592 nan 0.1000 -0.0006
- Fold10.Rep1: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3258 nan 0.0100 0.0029
2 1.3203 nan 0.0100 0.0029
3 1.3142 nan 0.0100 0.0029
4 1.3081 nan 0.0100 0.0028
5 1.3022 nan 0.0100 0.0027
6 1.2966 nan 0.0100 0.0026
7 1.2912 nan 0.0100 0.0026
8 1.2858 nan 0.0100 0.0026
9 1.2808 nan 0.0100 0.0026
10 1.2756 nan 0.0100 0.0025
20 1.2293 nan 0.0100 0.0020
40 1.1601 nan 0.0100 0.0014
60 1.1106 nan 0.0100 0.0010
80 1.0728 nan 0.0100 0.0008
100 1.0425 nan 0.0100 0.0007
120 1.0183 nan 0.0100 0.0003
140 0.9969 nan 0.0100 0.0004
160 0.9786 nan 0.0100 0.0004
180 0.9629 nan 0.0100 0.0002
200 0.9496 nan 0.0100 0.0002
220 0.9376 nan 0.0100 0.0002
240 0.9270 nan 0.0100 0.0002
260 0.9173 nan 0.0100 0.0001
280 0.9094 nan 0.0100 0.0001
300 0.9012 nan 0.0100 0.0001
320 0.8941 nan 0.0100 0.0001
340 0.8867 nan 0.0100 0.0001
360 0.8807 nan 0.0100 0.0002
380 0.8751 nan 0.0100 0.0000
400 0.8697 nan 0.0100 0.0001
420 0.8646 nan 0.0100 0.0000
440 0.8597 nan 0.0100 0.0000
460 0.8555 nan 0.0100 0.0001
480 0.8512 nan 0.0100 0.0000
500 0.8474 nan 0.0100 0.0001
520 0.8436 nan 0.0100 0.0000
540 0.8399 nan 0.0100 0.0000
560 0.8363 nan 0.0100 0.0000
580 0.8328 nan 0.0100 0.0000
600 0.8297 nan 0.0100 0.0000
620 0.8265 nan 0.0100 0.0000
640 0.8236 nan 0.0100 0.0000
660 0.8211 nan 0.0100 -0.0001
680 0.8185 nan 0.0100 -0.0000
700 0.8157 nan 0.0100 -0.0000
720 0.8131 nan 0.0100 -0.0000
740 0.8108 nan 0.0100 -0.0000
760 0.8086 nan 0.0100 -0.0000
780 0.8067 nan 0.0100 -0.0000
800 0.8042 nan 0.0100 -0.0001
820 0.8020 nan 0.0100 0.0000
840 0.7998 nan 0.0100 -0.0000
860 0.7977 nan 0.0100 -0.0000
880 0.7957 nan 0.0100 -0.0000
900 0.7938 nan 0.0100 -0.0001
920 0.7919 nan 0.0100 -0.0000
940 0.7902 nan 0.0100 -0.0000
960 0.7885 nan 0.0100 -0.0000
980 0.7870 nan 0.0100 -0.0001
1000 0.7853 nan 0.0100 -0.0000
1020 0.7836 nan 0.0100 -0.0000
1040 0.7822 nan 0.0100 -0.0000
1060 0.7806 nan 0.0100 -0.0001
1080 0.7790 nan 0.0100 -0.0000
1100 0.7775 nan 0.0100 -0.0001
- Fold01.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0036
2 1.3163 nan 0.0100 0.0036
3 1.3092 nan 0.0100 0.0035
4 1.3018 nan 0.0100 0.0037
5 1.2949 nan 0.0100 0.0035
6 1.2878 nan 0.0100 0.0035
7 1.2814 nan 0.0100 0.0035
8 1.2752 nan 0.0100 0.0029
9 1.2684 nan 0.0100 0.0033
10 1.2619 nan 0.0100 0.0031
20 1.2040 nan 0.0100 0.0026
40 1.1149 nan 0.0100 0.0019
60 1.0496 nan 0.0100 0.0013
80 1.0006 nan 0.0100 0.0010
100 0.9630 nan 0.0100 0.0008
120 0.9331 nan 0.0100 0.0006
140 0.9090 nan 0.0100 0.0004
160 0.8910 nan 0.0100 0.0002
180 0.8742 nan 0.0100 0.0002
200 0.8603 nan 0.0100 0.0002
220 0.8486 nan 0.0100 0.0001
240 0.8384 nan 0.0100 0.0002
260 0.8296 nan 0.0100 0.0000
280 0.8216 nan 0.0100 0.0000
300 0.8138 nan 0.0100 0.0001
320 0.8066 nan 0.0100 0.0001
340 0.7999 nan 0.0100 0.0000
360 0.7933 nan 0.0100 0.0000
380 0.7870 nan 0.0100 0.0002
400 0.7815 nan 0.0100 0.0000
420 0.7765 nan 0.0100 -0.0001
440 0.7717 nan 0.0100 0.0000
460 0.7669 nan 0.0100 0.0001
480 0.7628 nan 0.0100 -0.0001
500 0.7589 nan 0.0100 -0.0001
520 0.7555 nan 0.0100 0.0001
540 0.7518 nan 0.0100 -0.0000
560 0.7485 nan 0.0100 -0.0000
580 0.7454 nan 0.0100 0.0001
600 0.7421 nan 0.0100 -0.0000
620 0.7387 nan 0.0100 -0.0000
640 0.7353 nan 0.0100 0.0000
660 0.7323 nan 0.0100 -0.0001
680 0.7298 nan 0.0100 -0.0001
700 0.7270 nan 0.0100 -0.0000
720 0.7244 nan 0.0100 -0.0000
740 0.7219 nan 0.0100 -0.0001
760 0.7197 nan 0.0100 0.0000
780 0.7173 nan 0.0100 -0.0001
800 0.7151 nan 0.0100 -0.0000
820 0.7127 nan 0.0100 -0.0000
840 0.7106 nan 0.0100 -0.0002
860 0.7083 nan 0.0100 -0.0001
880 0.7059 nan 0.0100 -0.0001
900 0.7039 nan 0.0100 -0.0002
920 0.7020 nan 0.0100 -0.0001
940 0.6997 nan 0.0100 -0.0001
960 0.6977 nan 0.0100 -0.0000
980 0.6956 nan 0.0100 -0.0001
1000 0.6938 nan 0.0100 -0.0000
1020 0.6920 nan 0.0100 -0.0000
1040 0.6901 nan 0.0100 -0.0001
1060 0.6884 nan 0.0100 -0.0000
1080 0.6864 nan 0.0100 -0.0001
1100 0.6847 nan 0.0100 -0.0002
- Fold01.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3238 nan 0.0100 0.0038
2 1.3157 nan 0.0100 0.0037
3 1.3074 nan 0.0100 0.0040
4 1.3000 nan 0.0100 0.0036
5 1.2925 nan 0.0100 0.0039
6 1.2848 nan 0.0100 0.0035
7 1.2773 nan 0.0100 0.0037
8 1.2702 nan 0.0100 0.0035
9 1.2625 nan 0.0100 0.0035
10 1.2554 nan 0.0100 0.0035
20 1.1921 nan 0.0100 0.0028
40 1.0920 nan 0.0100 0.0020
60 1.0205 nan 0.0100 0.0015
80 0.9677 nan 0.0100 0.0007
100 0.9264 nan 0.0100 0.0009
120 0.8960 nan 0.0100 0.0006
140 0.8710 nan 0.0100 0.0005
160 0.8508 nan 0.0100 0.0005
180 0.8348 nan 0.0100 0.0002
200 0.8208 nan 0.0100 0.0003
220 0.8085 nan 0.0100 0.0001
240 0.7984 nan 0.0100 0.0002
260 0.7875 nan 0.0100 0.0001
280 0.7776 nan 0.0100 0.0000
300 0.7695 nan 0.0100 0.0001
320 0.7620 nan 0.0100 0.0001
340 0.7547 nan 0.0100 -0.0000
360 0.7484 nan 0.0100 -0.0002
380 0.7423 nan 0.0100 -0.0001
400 0.7369 nan 0.0100 -0.0000
420 0.7312 nan 0.0100 -0.0001
440 0.7257 nan 0.0100 -0.0001
460 0.7208 nan 0.0100 0.0000
480 0.7165 nan 0.0100 -0.0002
500 0.7118 nan 0.0100 -0.0000
520 0.7072 nan 0.0100 -0.0000
540 0.7029 nan 0.0100 -0.0001
560 0.6991 nan 0.0100 -0.0000
580 0.6953 nan 0.0100 -0.0001
600 0.6915 nan 0.0100 -0.0002
620 0.6880 nan 0.0100 0.0000
640 0.6844 nan 0.0100 -0.0001
660 0.6811 nan 0.0100 0.0001
680 0.6781 nan 0.0100 -0.0000
700 0.6753 nan 0.0100 -0.0001
720 0.6721 nan 0.0100 -0.0001
740 0.6692 nan 0.0100 -0.0000
760 0.6659 nan 0.0100 -0.0002
780 0.6622 nan 0.0100 -0.0000
800 0.6595 nan 0.0100 -0.0001
820 0.6569 nan 0.0100 -0.0000
840 0.6540 nan 0.0100 -0.0001
860 0.6510 nan 0.0100 -0.0001
880 0.6482 nan 0.0100 -0.0002
900 0.6460 nan 0.0100 0.0000
920 0.6431 nan 0.0100 0.0000
940 0.6403 nan 0.0100 -0.0001
960 0.6376 nan 0.0100 -0.0001
980 0.6350 nan 0.0100 -0.0001
1000 0.6323 nan 0.0100 -0.0001
1020 0.6295 nan 0.0100 -0.0001
1040 0.6267 nan 0.0100 -0.0001
1060 0.6246 nan 0.0100 -0.0001
1080 0.6222 nan 0.0100 -0.0001
1100 0.6198 nan 0.0100 -0.0001
- Fold01.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2709 nan 0.1000 0.0277
2 1.2237 nan 0.1000 0.0234
3 1.1882 nan 0.1000 0.0191
4 1.1583 nan 0.1000 0.0161
5 1.1354 nan 0.1000 0.0129
6 1.1094 nan 0.1000 0.0112
7 1.0900 nan 0.1000 0.0087
8 1.0754 nan 0.1000 0.0066
9 1.0557 nan 0.1000 0.0085
10 1.0410 nan 0.1000 0.0071
20 0.9473 nan 0.1000 0.0028
40 0.8702 nan 0.1000 0.0004
60 0.8291 nan 0.1000 -0.0004
80 0.8038 nan 0.1000 0.0001
100 0.7848 nan 0.1000 -0.0015
120 0.7717 nan 0.1000 -0.0005
140 0.7597 nan 0.1000 -0.0000
160 0.7524 nan 0.1000 -0.0006
180 0.7443 nan 0.1000 -0.0008
200 0.7374 nan 0.1000 -0.0007
220 0.7309 nan 0.1000 -0.0012
240 0.7269 nan 0.1000 -0.0010
260 0.7224 nan 0.1000 -0.0012
280 0.7180 nan 0.1000 -0.0006
300 0.7145 nan 0.1000 -0.0010
320 0.7095 nan 0.1000 -0.0011
340 0.7059 nan 0.1000 -0.0009
360 0.7007 nan 0.1000 -0.0004
380 0.6978 nan 0.1000 -0.0004
400 0.6939 nan 0.1000 -0.0011
420 0.6898 nan 0.1000 -0.0008
440 0.6868 nan 0.1000 -0.0009
460 0.6844 nan 0.1000 -0.0016
480 0.6811 nan 0.1000 -0.0005
500 0.6790 nan 0.1000 -0.0005
520 0.6762 nan 0.1000 -0.0007
540 0.6741 nan 0.1000 -0.0009
560 0.6718 nan 0.1000 -0.0005
580 0.6694 nan 0.1000 -0.0005
600 0.6681 nan 0.1000 -0.0012
620 0.6656 nan 0.1000 -0.0009
640 0.6637 nan 0.1000 -0.0005
660 0.6604 nan 0.1000 -0.0009
680 0.6589 nan 0.1000 -0.0003
700 0.6570 nan 0.1000 -0.0003
720 0.6560 nan 0.1000 -0.0007
740 0.6547 nan 0.1000 -0.0011
760 0.6531 nan 0.1000 -0.0015
780 0.6512 nan 0.1000 -0.0008
800 0.6494 nan 0.1000 -0.0005
820 0.6462 nan 0.1000 -0.0007
840 0.6442 nan 0.1000 -0.0002
860 0.6428 nan 0.1000 -0.0008
880 0.6415 nan 0.1000 -0.0007
900 0.6405 nan 0.1000 -0.0008
920 0.6393 nan 0.1000 -0.0008
940 0.6382 nan 0.1000 -0.0008
960 0.6361 nan 0.1000 -0.0002
980 0.6347 nan 0.1000 -0.0013
1000 0.6329 nan 0.1000 -0.0014
1020 0.6303 nan 0.1000 -0.0008
1040 0.6285 nan 0.1000 -0.0009
1060 0.6275 nan 0.1000 -0.0002
1080 0.6254 nan 0.1000 -0.0008
1100 0.6240 nan 0.1000 -0.0012
- Fold01.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2591 nan 0.1000 0.0361
2 1.1981 nan 0.1000 0.0280
3 1.1441 nan 0.1000 0.0240
4 1.1081 nan 0.1000 0.0209
5 1.0707 nan 0.1000 0.0167
6 1.0402 nan 0.1000 0.0136
7 1.0143 nan 0.1000 0.0119
8 0.9922 nan 0.1000 0.0105
9 0.9712 nan 0.1000 0.0099
10 0.9543 nan 0.1000 0.0086
20 0.8583 nan 0.1000 0.0013
40 0.7817 nan 0.1000 0.0015
60 0.7428 nan 0.1000 0.0002
80 0.7153 nan 0.1000 -0.0013
100 0.6906 nan 0.1000 -0.0010
120 0.6723 nan 0.1000 -0.0006
140 0.6542 nan 0.1000 -0.0014
160 0.6416 nan 0.1000 -0.0009
180 0.6298 nan 0.1000 -0.0006
200 0.6171 nan 0.1000 -0.0006
220 0.6052 nan 0.1000 -0.0009
240 0.5982 nan 0.1000 -0.0012
260 0.5899 nan 0.1000 -0.0007
280 0.5796 nan 0.1000 -0.0011
300 0.5705 nan 0.1000 -0.0015
320 0.5617 nan 0.1000 -0.0006
340 0.5555 nan 0.1000 -0.0011
360 0.5493 nan 0.1000 -0.0005
380 0.5423 nan 0.1000 -0.0014
400 0.5349 nan 0.1000 -0.0004
420 0.5305 nan 0.1000 -0.0009
440 0.5238 nan 0.1000 -0.0012
460 0.5182 nan 0.1000 -0.0004
480 0.5134 nan 0.1000 -0.0015
500 0.5058 nan 0.1000 -0.0006
520 0.4990 nan 0.1000 -0.0010
540 0.4928 nan 0.1000 -0.0011
560 0.4867 nan 0.1000 -0.0007
580 0.4801 nan 0.1000 -0.0010
600 0.4749 nan 0.1000 -0.0003
620 0.4708 nan 0.1000 -0.0003
640 0.4667 nan 0.1000 -0.0011
660 0.4625 nan 0.1000 -0.0011
680 0.4588 nan 0.1000 -0.0018
700 0.4550 nan 0.1000 -0.0010
720 0.4508 nan 0.1000 -0.0004
740 0.4463 nan 0.1000 -0.0007
760 0.4423 nan 0.1000 -0.0008
780 0.4381 nan 0.1000 -0.0009
800 0.4323 nan 0.1000 -0.0006
820 0.4287 nan 0.1000 -0.0010
840 0.4264 nan 0.1000 -0.0009
860 0.4224 nan 0.1000 -0.0010
880 0.4178 nan 0.1000 -0.0012
900 0.4149 nan 0.1000 -0.0012
920 0.4099 nan 0.1000 -0.0009
940 0.4070 nan 0.1000 -0.0009
960 0.4038 nan 0.1000 -0.0008
980 0.4021 nan 0.1000 -0.0006
1000 0.4001 nan 0.1000 -0.0009
1020 0.3966 nan 0.1000 -0.0010
1040 0.3928 nan 0.1000 -0.0012
1060 0.3893 nan 0.1000 -0.0008
1080 0.3868 nan 0.1000 -0.0009
1100 0.3837 nan 0.1000 -0.0008
- Fold01.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2556 nan 0.1000 0.0393
2 1.1935 nan 0.1000 0.0310
3 1.1336 nan 0.1000 0.0268
4 1.0860 nan 0.1000 0.0203
5 1.0488 nan 0.1000 0.0171
6 1.0163 nan 0.1000 0.0165
7 0.9888 nan 0.1000 0.0097
8 0.9635 nan 0.1000 0.0117
9 0.9404 nan 0.1000 0.0088
10 0.9221 nan 0.1000 0.0073
20 0.8173 nan 0.1000 -0.0001
40 0.7421 nan 0.1000 -0.0003
60 0.6947 nan 0.1000 -0.0005
80 0.6601 nan 0.1000 -0.0000
100 0.6339 nan 0.1000 -0.0006
120 0.6112 nan 0.1000 -0.0011
140 0.5914 nan 0.1000 -0.0028
160 0.5776 nan 0.1000 -0.0014
180 0.5581 nan 0.1000 -0.0011
200 0.5446 nan 0.1000 -0.0014
220 0.5290 nan 0.1000 -0.0019
240 0.5136 nan 0.1000 -0.0015
260 0.5013 nan 0.1000 -0.0020
280 0.4882 nan 0.1000 -0.0015
300 0.4760 nan 0.1000 -0.0009
320 0.4657 nan 0.1000 -0.0013
340 0.4551 nan 0.1000 -0.0009
360 0.4450 nan 0.1000 -0.0006
380 0.4347 nan 0.1000 -0.0010
400 0.4239 nan 0.1000 -0.0008
420 0.4142 nan 0.1000 -0.0017
440 0.4040 nan 0.1000 -0.0008
460 0.3962 nan 0.1000 -0.0012
480 0.3898 nan 0.1000 -0.0011
500 0.3804 nan 0.1000 -0.0008
520 0.3738 nan 0.1000 -0.0007
540 0.3688 nan 0.1000 -0.0017
560 0.3618 nan 0.1000 -0.0007
580 0.3543 nan 0.1000 -0.0011
600 0.3484 nan 0.1000 -0.0008
620 0.3421 nan 0.1000 -0.0007
640 0.3368 nan 0.1000 -0.0007
660 0.3308 nan 0.1000 -0.0008
680 0.3257 nan 0.1000 -0.0005
700 0.3203 nan 0.1000 -0.0006
720 0.3152 nan 0.1000 -0.0006
740 0.3099 nan 0.1000 -0.0003
760 0.3046 nan 0.1000 -0.0005
780 0.3016 nan 0.1000 -0.0012
800 0.2973 nan 0.1000 -0.0023
820 0.2928 nan 0.1000 -0.0007
840 0.2886 nan 0.1000 -0.0010
860 0.2850 nan 0.1000 -0.0005
880 0.2814 nan 0.1000 -0.0012
900 0.2783 nan 0.1000 -0.0010
920 0.2752 nan 0.1000 -0.0019
940 0.2706 nan 0.1000 -0.0005
960 0.2673 nan 0.1000 -0.0007
980 0.2639 nan 0.1000 -0.0010
1000 0.2612 nan 0.1000 -0.0009
1020 0.2584 nan 0.1000 -0.0012
1040 0.2557 nan 0.1000 -0.0018
1060 0.2521 nan 0.1000 -0.0005
1080 0.2483 nan 0.1000 -0.0005
1100 0.2454 nan 0.1000 -0.0008
- Fold01.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3258 nan 0.0100 0.0031
2 1.3199 nan 0.0100 0.0031
3 1.3138 nan 0.0100 0.0030
4 1.3082 nan 0.0100 0.0029
5 1.3033 nan 0.0100 0.0028
6 1.2972 nan 0.0100 0.0027
7 1.2916 nan 0.0100 0.0028
8 1.2857 nan 0.0100 0.0026
9 1.2808 nan 0.0100 0.0026
10 1.2753 nan 0.0100 0.0025
20 1.2281 nan 0.0100 0.0021
40 1.1544 nan 0.0100 0.0015
60 1.1038 nan 0.0100 0.0010
80 1.0664 nan 0.0100 0.0006
100 1.0353 nan 0.0100 0.0006
120 1.0100 nan 0.0100 0.0005
140 0.9895 nan 0.0100 0.0004
160 0.9713 nan 0.0100 0.0002
180 0.9559 nan 0.0100 0.0003
200 0.9431 nan 0.0100 0.0002
220 0.9316 nan 0.0100 0.0002
240 0.9214 nan 0.0100 0.0001
260 0.9126 nan 0.0100 0.0000
280 0.9048 nan 0.0100 0.0001
300 0.8971 nan 0.0100 0.0001
320 0.8908 nan 0.0100 0.0001
340 0.8847 nan 0.0100 0.0001
360 0.8788 nan 0.0100 -0.0000
380 0.8735 nan 0.0100 0.0000
400 0.8684 nan 0.0100 0.0000
420 0.8635 nan 0.0100 0.0000
440 0.8588 nan 0.0100 0.0000
460 0.8547 nan 0.0100 0.0000
480 0.8508 nan 0.0100 -0.0000
500 0.8470 nan 0.0100 0.0001
520 0.8434 nan 0.0100 -0.0001
540 0.8396 nan 0.0100 -0.0000
560 0.8361 nan 0.0100 -0.0000
580 0.8330 nan 0.0100 0.0000
600 0.8302 nan 0.0100 -0.0001
620 0.8275 nan 0.0100 -0.0000
640 0.8248 nan 0.0100 -0.0001
660 0.8220 nan 0.0100 -0.0000
680 0.8198 nan 0.0100 -0.0000
700 0.8172 nan 0.0100 0.0000
720 0.8147 nan 0.0100 -0.0001
740 0.8127 nan 0.0100 -0.0000
760 0.8106 nan 0.0100 -0.0000
780 0.8085 nan 0.0100 -0.0001
800 0.8066 nan 0.0100 -0.0000
820 0.8046 nan 0.0100 -0.0000
840 0.8027 nan 0.0100 -0.0001
860 0.8008 nan 0.0100 -0.0000
880 0.7989 nan 0.0100 -0.0000
900 0.7973 nan 0.0100 0.0000
920 0.7957 nan 0.0100 -0.0001
940 0.7940 nan 0.0100 0.0000
960 0.7925 nan 0.0100 -0.0001
980 0.7911 nan 0.0100 -0.0001
1000 0.7898 nan 0.0100 -0.0001
1020 0.7881 nan 0.0100 -0.0000
1040 0.7867 nan 0.0100 -0.0001
1060 0.7854 nan 0.0100 -0.0000
1080 0.7839 nan 0.0100 -0.0000
1100 0.7824 nan 0.0100 -0.0001
- Fold02.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3249 nan 0.0100 0.0037
2 1.3171 nan 0.0100 0.0033
3 1.3098 nan 0.0100 0.0035
4 1.3027 nan 0.0100 0.0036
5 1.2954 nan 0.0100 0.0035
6 1.2888 nan 0.0100 0.0031
7 1.2823 nan 0.0100 0.0030
8 1.2754 nan 0.0100 0.0031
9 1.2686 nan 0.0100 0.0031
10 1.2623 nan 0.0100 0.0031
20 1.2041 nan 0.0100 0.0024
40 1.1145 nan 0.0100 0.0019
60 1.0508 nan 0.0100 0.0013
80 1.0027 nan 0.0100 0.0010
100 0.9656 nan 0.0100 0.0007
120 0.9366 nan 0.0100 0.0006
140 0.9123 nan 0.0100 0.0003
160 0.8935 nan 0.0100 0.0003
180 0.8785 nan 0.0100 0.0003
200 0.8653 nan 0.0100 0.0002
220 0.8536 nan 0.0100 0.0001
240 0.8431 nan 0.0100 0.0002
260 0.8344 nan 0.0100 -0.0001
280 0.8265 nan 0.0100 0.0001
300 0.8197 nan 0.0100 0.0000
320 0.8121 nan 0.0100 0.0001
340 0.8056 nan 0.0100 -0.0000
360 0.7995 nan 0.0100 0.0001
380 0.7942 nan 0.0100 -0.0000
400 0.7888 nan 0.0100 -0.0001
420 0.7834 nan 0.0100 -0.0001
440 0.7791 nan 0.0100 -0.0001
460 0.7741 nan 0.0100 -0.0000
480 0.7703 nan 0.0100 -0.0001
500 0.7662 nan 0.0100 0.0000
520 0.7624 nan 0.0100 -0.0002
540 0.7586 nan 0.0100 -0.0001
560 0.7554 nan 0.0100 -0.0001
580 0.7518 nan 0.0100 0.0000
600 0.7488 nan 0.0100 -0.0000
620 0.7455 nan 0.0100 -0.0000
640 0.7426 nan 0.0100 -0.0001
660 0.7399 nan 0.0100 -0.0000
680 0.7370 nan 0.0100 -0.0001
700 0.7340 nan 0.0100 -0.0001
720 0.7316 nan 0.0100 -0.0001
740 0.7292 nan 0.0100 -0.0000
760 0.7265 nan 0.0100 -0.0001
780 0.7242 nan 0.0100 -0.0001
800 0.7219 nan 0.0100 -0.0002
820 0.7197 nan 0.0100 -0.0000
840 0.7174 nan 0.0100 0.0000
860 0.7150 nan 0.0100 -0.0001
880 0.7129 nan 0.0100 -0.0001
900 0.7106 nan 0.0100 -0.0000
920 0.7083 nan 0.0100 0.0000
940 0.7057 nan 0.0100 -0.0001
960 0.7036 nan 0.0100 -0.0002
980 0.7014 nan 0.0100 0.0000
1000 0.6995 nan 0.0100 -0.0001
1020 0.6976 nan 0.0100 -0.0000
1040 0.6958 nan 0.0100 -0.0000
1060 0.6940 nan 0.0100 -0.0002
1080 0.6917 nan 0.0100 -0.0001
1100 0.6896 nan 0.0100 -0.0001
- Fold02.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3234 nan 0.0100 0.0040
2 1.3153 nan 0.0100 0.0037
3 1.3076 nan 0.0100 0.0034
4 1.2997 nan 0.0100 0.0040
5 1.2919 nan 0.0100 0.0034
6 1.2841 nan 0.0100 0.0037
7 1.2767 nan 0.0100 0.0037
8 1.2693 nan 0.0100 0.0036
9 1.2624 nan 0.0100 0.0034
10 1.2558 nan 0.0100 0.0034
20 1.1922 nan 0.0100 0.0029
40 1.0956 nan 0.0100 0.0017
60 1.0241 nan 0.0100 0.0015
80 0.9713 nan 0.0100 0.0009
100 0.9308 nan 0.0100 0.0008
120 0.8997 nan 0.0100 0.0006
140 0.8755 nan 0.0100 0.0004
160 0.8557 nan 0.0100 0.0002
180 0.8395 nan 0.0100 0.0003
200 0.8258 nan 0.0100 0.0001
220 0.8134 nan 0.0100 0.0001
240 0.8022 nan 0.0100 0.0002
260 0.7931 nan 0.0100 0.0000
280 0.7843 nan 0.0100 0.0001
300 0.7764 nan 0.0100 0.0001
320 0.7693 nan 0.0100 0.0001
340 0.7624 nan 0.0100 -0.0000
360 0.7561 nan 0.0100 -0.0001
380 0.7503 nan 0.0100 -0.0000
400 0.7448 nan 0.0100 -0.0001
420 0.7393 nan 0.0100 -0.0002
440 0.7345 nan 0.0100 -0.0000
460 0.7295 nan 0.0100 -0.0000
480 0.7245 nan 0.0100 -0.0001
500 0.7205 nan 0.0100 -0.0000
520 0.7168 nan 0.0100 -0.0001
540 0.7126 nan 0.0100 -0.0002
560 0.7086 nan 0.0100 -0.0001
580 0.7053 nan 0.0100 -0.0002
600 0.7014 nan 0.0100 -0.0000
620 0.6977 nan 0.0100 -0.0001
640 0.6940 nan 0.0100 -0.0000
660 0.6905 nan 0.0100 -0.0001
680 0.6866 nan 0.0100 0.0000
700 0.6831 nan 0.0100 -0.0001
720 0.6802 nan 0.0100 -0.0002
740 0.6767 nan 0.0100 -0.0001
760 0.6736 nan 0.0100 -0.0001
780 0.6701 nan 0.0100 -0.0000
800 0.6675 nan 0.0100 -0.0001
820 0.6646 nan 0.0100 -0.0002
840 0.6623 nan 0.0100 -0.0002
860 0.6595 nan 0.0100 -0.0001
880 0.6566 nan 0.0100 -0.0001
900 0.6538 nan 0.0100 -0.0001
920 0.6510 nan 0.0100 -0.0000
940 0.6481 nan 0.0100 -0.0001
960 0.6453 nan 0.0100 -0.0000
980 0.6426 nan 0.0100 -0.0002
1000 0.6398 nan 0.0100 -0.0000
1020 0.6380 nan 0.0100 -0.0001
1040 0.6355 nan 0.0100 -0.0001
1060 0.6329 nan 0.0100 -0.0000
1080 0.6305 nan 0.0100 -0.0001
1100 0.6279 nan 0.0100 -0.0001
- Fold02.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2715 nan 0.1000 0.0293
2 1.2178 nan 0.1000 0.0240
3 1.1785 nan 0.1000 0.0200
4 1.1445 nan 0.1000 0.0163
5 1.1184 nan 0.1000 0.0127
6 1.1047 nan 0.1000 0.0047
7 1.0831 nan 0.1000 0.0106
8 1.0610 nan 0.1000 0.0076
9 1.0440 nan 0.1000 0.0061
10 1.0309 nan 0.1000 0.0067
20 0.9405 nan 0.1000 0.0011
40 0.8679 nan 0.1000 0.0010
60 0.8294 nan 0.1000 -0.0010
80 0.8074 nan 0.1000 0.0002
100 0.7918 nan 0.1000 -0.0001
120 0.7783 nan 0.1000 -0.0001
140 0.7680 nan 0.1000 -0.0007
160 0.7601 nan 0.1000 -0.0011
180 0.7531 nan 0.1000 -0.0005
200 0.7472 nan 0.1000 -0.0009
220 0.7416 nan 0.1000 -0.0010
240 0.7357 nan 0.1000 -0.0006
260 0.7280 nan 0.1000 -0.0008
280 0.7236 nan 0.1000 -0.0004
300 0.7190 nan 0.1000 -0.0005
320 0.7146 nan 0.1000 -0.0017
340 0.7098 nan 0.1000 -0.0009
360 0.7064 nan 0.1000 -0.0003
380 0.7044 nan 0.1000 -0.0003
400 0.7010 nan 0.1000 -0.0009
420 0.6979 nan 0.1000 -0.0009
440 0.6952 nan 0.1000 -0.0005
460 0.6934 nan 0.1000 -0.0008
480 0.6899 nan 0.1000 -0.0007
500 0.6879 nan 0.1000 -0.0005
520 0.6838 nan 0.1000 -0.0003
540 0.6827 nan 0.1000 -0.0013
560 0.6800 nan 0.1000 -0.0008
580 0.6776 nan 0.1000 -0.0009
600 0.6755 nan 0.1000 -0.0009
620 0.6734 nan 0.1000 -0.0011
640 0.6707 nan 0.1000 -0.0005
660 0.6685 nan 0.1000 -0.0010
680 0.6670 nan 0.1000 -0.0009
700 0.6660 nan 0.1000 -0.0007
720 0.6649 nan 0.1000 -0.0002
740 0.6621 nan 0.1000 -0.0005
760 0.6605 nan 0.1000 -0.0007
780 0.6586 nan 0.1000 -0.0007
800 0.6573 nan 0.1000 -0.0005
820 0.6552 nan 0.1000 -0.0011
840 0.6537 nan 0.1000 -0.0004
860 0.6513 nan 0.1000 -0.0008
880 0.6508 nan 0.1000 -0.0005
900 0.6488 nan 0.1000 -0.0008
920 0.6470 nan 0.1000 -0.0008
940 0.6450 nan 0.1000 -0.0008
960 0.6436 nan 0.1000 -0.0006
980 0.6421 nan 0.1000 -0.0008
1000 0.6407 nan 0.1000 -0.0008
1020 0.6398 nan 0.1000 -0.0013
1040 0.6382 nan 0.1000 -0.0003
1060 0.6370 nan 0.1000 -0.0010
1080 0.6358 nan 0.1000 -0.0006
1100 0.6349 nan 0.1000 -0.0004
- Fold02.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2580 nan 0.1000 0.0343
2 1.1981 nan 0.1000 0.0271
3 1.1509 nan 0.1000 0.0258
4 1.1104 nan 0.1000 0.0198
5 1.0732 nan 0.1000 0.0182
6 1.0435 nan 0.1000 0.0147
7 1.0181 nan 0.1000 0.0128
8 0.9926 nan 0.1000 0.0112
9 0.9730 nan 0.1000 0.0087
10 0.9527 nan 0.1000 0.0078
20 0.8607 nan 0.1000 0.0024
40 0.7888 nan 0.1000 -0.0005
60 0.7490 nan 0.1000 -0.0009
80 0.7231 nan 0.1000 -0.0007
100 0.6995 nan 0.1000 -0.0016
120 0.6803 nan 0.1000 -0.0003
140 0.6638 nan 0.1000 -0.0010
160 0.6486 nan 0.1000 -0.0008
180 0.6339 nan 0.1000 -0.0005
200 0.6247 nan 0.1000 -0.0008
220 0.6143 nan 0.1000 -0.0004
240 0.6053 nan 0.1000 -0.0018
260 0.5944 nan 0.1000 -0.0004
280 0.5841 nan 0.1000 -0.0001
300 0.5751 nan 0.1000 -0.0020
320 0.5660 nan 0.1000 -0.0005
340 0.5608 nan 0.1000 -0.0006
360 0.5521 nan 0.1000 -0.0012
380 0.5472 nan 0.1000 -0.0010
400 0.5414 nan 0.1000 -0.0011
420 0.5350 nan 0.1000 -0.0007
440 0.5274 nan 0.1000 -0.0016
460 0.5229 nan 0.1000 -0.0002
480 0.5168 nan 0.1000 -0.0010
500 0.5089 nan 0.1000 -0.0009
520 0.5014 nan 0.1000 -0.0010
540 0.4980 nan 0.1000 -0.0008
560 0.4921 nan 0.1000 -0.0008
580 0.4862 nan 0.1000 -0.0009
600 0.4815 nan 0.1000 -0.0012
620 0.4767 nan 0.1000 -0.0012
640 0.4716 nan 0.1000 -0.0006
660 0.4664 nan 0.1000 -0.0009
680 0.4604 nan 0.1000 -0.0003
700 0.4561 nan 0.1000 -0.0009
720 0.4520 nan 0.1000 -0.0009
740 0.4472 nan 0.1000 -0.0005
760 0.4433 nan 0.1000 -0.0013
780 0.4396 nan 0.1000 -0.0008
800 0.4366 nan 0.1000 -0.0010
820 0.4336 nan 0.1000 -0.0011
840 0.4315 nan 0.1000 -0.0004
860 0.4291 nan 0.1000 -0.0006
880 0.4248 nan 0.1000 -0.0007
900 0.4207 nan 0.1000 -0.0008
920 0.4168 nan 0.1000 -0.0010
940 0.4130 nan 0.1000 -0.0009
960 0.4088 nan 0.1000 -0.0007
980 0.4062 nan 0.1000 -0.0016
1000 0.4034 nan 0.1000 -0.0013
1020 0.3999 nan 0.1000 -0.0006
1040 0.3960 nan 0.1000 -0.0005
1060 0.3923 nan 0.1000 -0.0010
1080 0.3890 nan 0.1000 -0.0009
1100 0.3862 nan 0.1000 -0.0008
- Fold02.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2503 nan 0.1000 0.0389
2 1.1832 nan 0.1000 0.0313
3 1.1333 nan 0.1000 0.0239
4 1.0852 nan 0.1000 0.0224
5 1.0467 nan 0.1000 0.0191
6 1.0165 nan 0.1000 0.0144
7 0.9906 nan 0.1000 0.0112
8 0.9632 nan 0.1000 0.0117
9 0.9416 nan 0.1000 0.0096
10 0.9248 nan 0.1000 0.0064
20 0.8220 nan 0.1000 0.0013
40 0.7429 nan 0.1000 -0.0009
60 0.7061 nan 0.1000 -0.0016
80 0.6771 nan 0.1000 -0.0024
100 0.6510 nan 0.1000 -0.0002
120 0.6283 nan 0.1000 0.0003
140 0.6048 nan 0.1000 -0.0010
160 0.5845 nan 0.1000 -0.0012
180 0.5641 nan 0.1000 -0.0010
200 0.5518 nan 0.1000 -0.0018
220 0.5366 nan 0.1000 -0.0021
240 0.5272 nan 0.1000 -0.0018
260 0.5186 nan 0.1000 -0.0005
280 0.5075 nan 0.1000 -0.0017
300 0.4933 nan 0.1000 -0.0014
320 0.4829 nan 0.1000 -0.0009
340 0.4744 nan 0.1000 -0.0015
360 0.4626 nan 0.1000 -0.0009
380 0.4512 nan 0.1000 -0.0005
400 0.4413 nan 0.1000 -0.0012
420 0.4357 nan 0.1000 -0.0014
440 0.4262 nan 0.1000 -0.0006
460 0.4155 nan 0.1000 -0.0015
480 0.4071 nan 0.1000 -0.0008
500 0.4003 nan 0.1000 -0.0018
520 0.3916 nan 0.1000 -0.0015
540 0.3866 nan 0.1000 -0.0006
560 0.3803 nan 0.1000 -0.0010
580 0.3725 nan 0.1000 -0.0013
600 0.3662 nan 0.1000 -0.0007
620 0.3587 nan 0.1000 -0.0013
640 0.3540 nan 0.1000 -0.0015
660 0.3487 nan 0.1000 -0.0008
680 0.3438 nan 0.1000 -0.0008
700 0.3375 nan 0.1000 -0.0007
720 0.3336 nan 0.1000 -0.0011
740 0.3290 nan 0.1000 -0.0004
760 0.3233 nan 0.1000 -0.0009
780 0.3186 nan 0.1000 -0.0008
800 0.3146 nan 0.1000 -0.0011
820 0.3105 nan 0.1000 -0.0010
840 0.3061 nan 0.1000 -0.0008
860 0.3010 nan 0.1000 -0.0008
880 0.2963 nan 0.1000 -0.0014
900 0.2923 nan 0.1000 -0.0009
920 0.2878 nan 0.1000 -0.0008
940 0.2842 nan 0.1000 -0.0007
960 0.2792 nan 0.1000 -0.0010
980 0.2755 nan 0.1000 -0.0008
1000 0.2725 nan 0.1000 -0.0010
1020 0.2688 nan 0.1000 -0.0006
1040 0.2663 nan 0.1000 -0.0004
1060 0.2620 nan 0.1000 -0.0001
1080 0.2582 nan 0.1000 -0.0007
1100 0.2543 nan 0.1000 -0.0007
- Fold02.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3263 nan 0.0100 0.0029
2 1.3205 nan 0.0100 0.0028
3 1.3148 nan 0.0100 0.0027
4 1.3093 nan 0.0100 0.0026
5 1.3041 nan 0.0100 0.0027
6 1.2988 nan 0.0100 0.0026
7 1.2938 nan 0.0100 0.0026
8 1.2885 nan 0.0100 0.0025
9 1.2839 nan 0.0100 0.0025
10 1.2795 nan 0.0100 0.0024
20 1.2350 nan 0.0100 0.0020
40 1.1668 nan 0.0100 0.0013
60 1.1214 nan 0.0100 0.0004
80 1.0868 nan 0.0100 0.0007
100 1.0590 nan 0.0100 0.0005
120 1.0366 nan 0.0100 0.0004
140 1.0173 nan 0.0100 0.0003
160 1.0015 nan 0.0100 0.0003
180 0.9877 nan 0.0100 0.0002
200 0.9754 nan 0.0100 0.0003
220 0.9644 nan 0.0100 0.0002
240 0.9549 nan 0.0100 0.0002
260 0.9465 nan 0.0100 0.0002
280 0.9384 nan 0.0100 0.0001
300 0.9312 nan 0.0100 0.0001
320 0.9245 nan 0.0100 0.0001
340 0.9178 nan 0.0100 0.0001
360 0.9122 nan 0.0100 0.0000
380 0.9070 nan 0.0100 0.0001
400 0.9020 nan 0.0100 0.0001
420 0.8977 nan 0.0100 0.0001
440 0.8938 nan 0.0100 0.0001
460 0.8895 nan 0.0100 0.0000
480 0.8859 nan 0.0100 -0.0000
500 0.8823 nan 0.0100 0.0000
520 0.8787 nan 0.0100 0.0000
540 0.8753 nan 0.0100 -0.0000
560 0.8722 nan 0.0100 -0.0001
580 0.8692 nan 0.0100 0.0000
600 0.8662 nan 0.0100 0.0000
620 0.8637 nan 0.0100 -0.0001
640 0.8611 nan 0.0100 -0.0000
660 0.8586 nan 0.0100 0.0000
680 0.8562 nan 0.0100 -0.0000
700 0.8539 nan 0.0100 0.0000
720 0.8514 nan 0.0100 -0.0000
740 0.8490 nan 0.0100 -0.0000
760 0.8467 nan 0.0100 -0.0001
780 0.8444 nan 0.0100 -0.0000
800 0.8424 nan 0.0100 0.0000
820 0.8403 nan 0.0100 -0.0000
840 0.8384 nan 0.0100 -0.0000
860 0.8366 nan 0.0100 -0.0000
880 0.8348 nan 0.0100 -0.0000
900 0.8333 nan 0.0100 -0.0000
920 0.8317 nan 0.0100 -0.0001
940 0.8299 nan 0.0100 -0.0000
960 0.8283 nan 0.0100 -0.0003
980 0.8268 nan 0.0100 -0.0000
1000 0.8251 nan 0.0100 -0.0001
1020 0.8237 nan 0.0100 -0.0001
1040 0.8222 nan 0.0100 -0.0000
1060 0.8209 nan 0.0100 -0.0000
1080 0.8195 nan 0.0100 -0.0001
1100 0.8180 nan 0.0100 -0.0000
- Fold03.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3249 nan 0.0100 0.0034
2 1.3182 nan 0.0100 0.0034
3 1.3109 nan 0.0100 0.0037
4 1.3042 nan 0.0100 0.0036
5 1.2976 nan 0.0100 0.0031
6 1.2907 nan 0.0100 0.0031
7 1.2839 nan 0.0100 0.0031
8 1.2779 nan 0.0100 0.0033
9 1.2715 nan 0.0100 0.0029
10 1.2655 nan 0.0100 0.0030
20 1.2114 nan 0.0100 0.0024
40 1.1256 nan 0.0100 0.0016
60 1.0645 nan 0.0100 0.0012
80 1.0197 nan 0.0100 0.0009
100 0.9856 nan 0.0100 0.0007
120 0.9584 nan 0.0100 0.0005
140 0.9369 nan 0.0100 0.0004
160 0.9198 nan 0.0100 0.0002
180 0.9054 nan 0.0100 0.0001
200 0.8937 nan 0.0100 0.0000
220 0.8834 nan 0.0100 0.0000
240 0.8747 nan 0.0100 -0.0001
260 0.8662 nan 0.0100 0.0001
280 0.8579 nan 0.0100 -0.0000
300 0.8505 nan 0.0100 0.0002
320 0.8439 nan 0.0100 -0.0000
340 0.8378 nan 0.0100 0.0001
360 0.8317 nan 0.0100 0.0001
380 0.8262 nan 0.0100 0.0000
400 0.8207 nan 0.0100 0.0000
420 0.8155 nan 0.0100 -0.0000
440 0.8111 nan 0.0100 -0.0000
460 0.8068 nan 0.0100 0.0000
480 0.8020 nan 0.0100 -0.0001
500 0.7982 nan 0.0100 -0.0001
520 0.7944 nan 0.0100 -0.0001
540 0.7907 nan 0.0100 0.0000
560 0.7875 nan 0.0100 -0.0001
580 0.7843 nan 0.0100 -0.0001
600 0.7811 nan 0.0100 -0.0000
620 0.7782 nan 0.0100 -0.0001
640 0.7750 nan 0.0100 -0.0001
660 0.7723 nan 0.0100 0.0000
680 0.7694 nan 0.0100 -0.0001
700 0.7670 nan 0.0100 -0.0000
720 0.7646 nan 0.0100 -0.0000
740 0.7617 nan 0.0100 -0.0001
760 0.7590 nan 0.0100 -0.0001
780 0.7565 nan 0.0100 -0.0001
800 0.7538 nan 0.0100 -0.0001
820 0.7514 nan 0.0100 -0.0001
840 0.7492 nan 0.0100 -0.0000
860 0.7467 nan 0.0100 -0.0000
880 0.7442 nan 0.0100 -0.0001
900 0.7420 nan 0.0100 -0.0000
920 0.7399 nan 0.0100 -0.0001
940 0.7375 nan 0.0100 -0.0000
960 0.7358 nan 0.0100 -0.0001
980 0.7341 nan 0.0100 -0.0001
1000 0.7317 nan 0.0100 -0.0000
1020 0.7296 nan 0.0100 -0.0001
1040 0.7275 nan 0.0100 -0.0002
1060 0.7253 nan 0.0100 -0.0001
1080 0.7237 nan 0.0100 -0.0001
1100 0.7217 nan 0.0100 -0.0001
- Fold03.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0037
2 1.3168 nan 0.0100 0.0035
3 1.3095 nan 0.0100 0.0037
4 1.3026 nan 0.0100 0.0036
5 1.2954 nan 0.0100 0.0034
6 1.2885 nan 0.0100 0.0034
7 1.2813 nan 0.0100 0.0033
8 1.2750 nan 0.0100 0.0035
9 1.2686 nan 0.0100 0.0032
10 1.2624 nan 0.0100 0.0028
20 1.2032 nan 0.0100 0.0026
40 1.1116 nan 0.0100 0.0018
60 1.0438 nan 0.0100 0.0013
80 0.9936 nan 0.0100 0.0008
100 0.9553 nan 0.0100 0.0007
120 0.9260 nan 0.0100 0.0006
140 0.9035 nan 0.0100 0.0003
160 0.8845 nan 0.0100 0.0002
180 0.8690 nan 0.0100 0.0002
200 0.8546 nan 0.0100 0.0001
220 0.8423 nan 0.0100 0.0001
240 0.8312 nan 0.0100 -0.0001
260 0.8214 nan 0.0100 0.0001
280 0.8132 nan 0.0100 -0.0000
300 0.8059 nan 0.0100 -0.0001
320 0.7991 nan 0.0100 0.0000
340 0.7923 nan 0.0100 -0.0000
360 0.7865 nan 0.0100 0.0000
380 0.7807 nan 0.0100 0.0001
400 0.7747 nan 0.0100 0.0000
420 0.7692 nan 0.0100 -0.0001
440 0.7643 nan 0.0100 0.0000
460 0.7593 nan 0.0100 -0.0000
480 0.7547 nan 0.0100 -0.0000
500 0.7502 nan 0.0100 -0.0001
520 0.7459 nan 0.0100 -0.0000
540 0.7421 nan 0.0100 -0.0001
560 0.7376 nan 0.0100 -0.0001
580 0.7339 nan 0.0100 -0.0001
600 0.7298 nan 0.0100 -0.0001
620 0.7257 nan 0.0100 -0.0000
640 0.7222 nan 0.0100 -0.0000
660 0.7183 nan 0.0100 -0.0001
680 0.7152 nan 0.0100 -0.0000
700 0.7116 nan 0.0100 -0.0001
720 0.7082 nan 0.0100 -0.0001
740 0.7053 nan 0.0100 -0.0001
760 0.7023 nan 0.0100 -0.0001
780 0.6995 nan 0.0100 -0.0001
800 0.6968 nan 0.0100 -0.0001
820 0.6938 nan 0.0100 -0.0002
840 0.6907 nan 0.0100 -0.0000
860 0.6878 nan 0.0100 -0.0000
880 0.6849 nan 0.0100 -0.0001
900 0.6827 nan 0.0100 -0.0001
920 0.6796 nan 0.0100 -0.0001
940 0.6772 nan 0.0100 -0.0001
960 0.6743 nan 0.0100 -0.0001
980 0.6716 nan 0.0100 -0.0002
1000 0.6686 nan 0.0100 -0.0000
1020 0.6658 nan 0.0100 -0.0002
1040 0.6638 nan 0.0100 -0.0001
1060 0.6612 nan 0.0100 -0.0001
1080 0.6584 nan 0.0100 -0.0000
1100 0.6556 nan 0.0100 -0.0001
- Fold03.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2752 nan 0.1000 0.0262
2 1.2262 nan 0.1000 0.0198
3 1.1894 nan 0.1000 0.0185
4 1.1555 nan 0.1000 0.0146
5 1.1314 nan 0.1000 0.0120
6 1.1097 nan 0.1000 0.0098
7 1.0927 nan 0.1000 0.0063
8 1.0777 nan 0.1000 0.0079
9 1.0662 nan 0.1000 0.0066
10 1.0544 nan 0.1000 0.0052
20 0.9740 nan 0.1000 0.0029
40 0.9033 nan 0.1000 0.0011
60 0.8682 nan 0.1000 -0.0002
80 0.8437 nan 0.1000 0.0003
100 0.8261 nan 0.1000 -0.0004
120 0.8154 nan 0.1000 -0.0004
140 0.8021 nan 0.1000 -0.0004
160 0.7945 nan 0.1000 -0.0004
180 0.7885 nan 0.1000 -0.0007
200 0.7807 nan 0.1000 -0.0014
220 0.7732 nan 0.1000 -0.0011
240 0.7665 nan 0.1000 -0.0011
260 0.7610 nan 0.1000 -0.0001
280 0.7549 nan 0.1000 -0.0008
300 0.7517 nan 0.1000 -0.0009
320 0.7466 nan 0.1000 -0.0003
340 0.7439 nan 0.1000 -0.0004
360 0.7390 nan 0.1000 -0.0011
380 0.7369 nan 0.1000 -0.0003
400 0.7335 nan 0.1000 -0.0005
420 0.7286 nan 0.1000 -0.0011
440 0.7254 nan 0.1000 -0.0004
460 0.7208 nan 0.1000 -0.0004
480 0.7173 nan 0.1000 -0.0005
500 0.7156 nan 0.1000 -0.0007
520 0.7139 nan 0.1000 -0.0005
540 0.7117 nan 0.1000 -0.0009
560 0.7094 nan 0.1000 -0.0007
580 0.7060 nan 0.1000 -0.0004
600 0.7040 nan 0.1000 -0.0006
620 0.7012 nan 0.1000 -0.0005
640 0.7002 nan 0.1000 -0.0007
660 0.6988 nan 0.1000 -0.0003
680 0.6969 nan 0.1000 -0.0008
700 0.6952 nan 0.1000 -0.0009
720 0.6929 nan 0.1000 -0.0004
740 0.6910 nan 0.1000 -0.0007
760 0.6886 nan 0.1000 -0.0010
780 0.6862 nan 0.1000 -0.0007
800 0.6841 nan 0.1000 -0.0004
820 0.6809 nan 0.1000 -0.0003
840 0.6797 nan 0.1000 -0.0011
860 0.6787 nan 0.1000 -0.0010
880 0.6761 nan 0.1000 -0.0006
900 0.6740 nan 0.1000 -0.0007
920 0.6718 nan 0.1000 -0.0012
940 0.6699 nan 0.1000 -0.0005
960 0.6682 nan 0.1000 -0.0017
980 0.6677 nan 0.1000 -0.0009
1000 0.6657 nan 0.1000 -0.0003
1020 0.6639 nan 0.1000 -0.0007
1040 0.6627 nan 0.1000 -0.0008
1060 0.6610 nan 0.1000 -0.0006
1080 0.6587 nan 0.1000 -0.0005
1100 0.6566 nan 0.1000 -0.0007
- Fold03.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2627 nan 0.1000 0.0318
2 1.2040 nan 0.1000 0.0265
3 1.1628 nan 0.1000 0.0221
4 1.1191 nan 0.1000 0.0189
5 1.0863 nan 0.1000 0.0162
6 1.0588 nan 0.1000 0.0129
7 1.0358 nan 0.1000 0.0111
8 1.0127 nan 0.1000 0.0095
9 0.9945 nan 0.1000 0.0082
10 0.9786 nan 0.1000 0.0072
20 0.8917 nan 0.1000 0.0017
40 0.8193 nan 0.1000 -0.0005
60 0.7848 nan 0.1000 -0.0009
80 0.7565 nan 0.1000 -0.0009
100 0.7296 nan 0.1000 -0.0018
120 0.7143 nan 0.1000 -0.0005
140 0.6983 nan 0.1000 -0.0007
160 0.6808 nan 0.1000 -0.0002
180 0.6661 nan 0.1000 -0.0009
200 0.6542 nan 0.1000 -0.0009
220 0.6403 nan 0.1000 -0.0013
240 0.6308 nan 0.1000 -0.0011
260 0.6222 nan 0.1000 -0.0013
280 0.6148 nan 0.1000 -0.0003
300 0.6038 nan 0.1000 -0.0013
320 0.5950 nan 0.1000 -0.0007
340 0.5846 nan 0.1000 -0.0010
360 0.5772 nan 0.1000 -0.0009
380 0.5676 nan 0.1000 -0.0009
400 0.5578 nan 0.1000 -0.0009
420 0.5497 nan 0.1000 -0.0005
440 0.5459 nan 0.1000 -0.0011
460 0.5393 nan 0.1000 -0.0009
480 0.5326 nan 0.1000 -0.0009
500 0.5265 nan 0.1000 -0.0007
520 0.5223 nan 0.1000 -0.0016
540 0.5182 nan 0.1000 -0.0016
560 0.5110 nan 0.1000 -0.0008
580 0.5061 nan 0.1000 -0.0007
600 0.5020 nan 0.1000 -0.0012
620 0.4970 nan 0.1000 -0.0012
640 0.4911 nan 0.1000 -0.0012
660 0.4862 nan 0.1000 -0.0002
680 0.4792 nan 0.1000 -0.0013
700 0.4775 nan 0.1000 -0.0009
720 0.4720 nan 0.1000 -0.0006
740 0.4662 nan 0.1000 -0.0008
760 0.4625 nan 0.1000 -0.0012
780 0.4582 nan 0.1000 -0.0009
800 0.4541 nan 0.1000 -0.0004
820 0.4510 nan 0.1000 -0.0008
840 0.4470 nan 0.1000 -0.0003
860 0.4436 nan 0.1000 -0.0008
880 0.4403 nan 0.1000 -0.0005
900 0.4367 nan 0.1000 -0.0012
920 0.4331 nan 0.1000 -0.0010
940 0.4292 nan 0.1000 -0.0010
960 0.4254 nan 0.1000 -0.0004
980 0.4202 nan 0.1000 -0.0007
1000 0.4162 nan 0.1000 -0.0006
1020 0.4147 nan 0.1000 -0.0016
1040 0.4114 nan 0.1000 -0.0011
1060 0.4086 nan 0.1000 -0.0007
1080 0.4048 nan 0.1000 -0.0007
1100 0.4037 nan 0.1000 -0.0007
- Fold03.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2593 nan 0.1000 0.0329
2 1.2000 nan 0.1000 0.0308
3 1.1549 nan 0.1000 0.0219
4 1.1085 nan 0.1000 0.0213
5 1.0720 nan 0.1000 0.0189
6 1.0430 nan 0.1000 0.0129
7 1.0130 nan 0.1000 0.0131
8 0.9900 nan 0.1000 0.0094
9 0.9712 nan 0.1000 0.0071
10 0.9533 nan 0.1000 0.0076
20 0.8558 nan 0.1000 0.0007
40 0.7773 nan 0.1000 -0.0009
60 0.7394 nan 0.1000 -0.0008
80 0.7080 nan 0.1000 -0.0017
100 0.6746 nan 0.1000 -0.0002
120 0.6486 nan 0.1000 -0.0018
140 0.6284 nan 0.1000 -0.0007
160 0.6115 nan 0.1000 0.0003
180 0.5935 nan 0.1000 -0.0017
200 0.5771 nan 0.1000 -0.0007
220 0.5623 nan 0.1000 -0.0011
240 0.5463 nan 0.1000 -0.0015
260 0.5322 nan 0.1000 -0.0011
280 0.5192 nan 0.1000 -0.0009
300 0.5073 nan 0.1000 -0.0011
320 0.4977 nan 0.1000 -0.0018
340 0.4882 nan 0.1000 -0.0008
360 0.4783 nan 0.1000 -0.0018
380 0.4688 nan 0.1000 -0.0007
400 0.4617 nan 0.1000 -0.0011
420 0.4512 nan 0.1000 -0.0009
440 0.4441 nan 0.1000 -0.0009
460 0.4359 nan 0.1000 -0.0015
480 0.4268 nan 0.1000 -0.0011
500 0.4197 nan 0.1000 -0.0006
520 0.4128 nan 0.1000 -0.0008
540 0.4044 nan 0.1000 -0.0009
560 0.3977 nan 0.1000 -0.0012
580 0.3909 nan 0.1000 -0.0007
600 0.3849 nan 0.1000 -0.0006
620 0.3790 nan 0.1000 -0.0005
640 0.3739 nan 0.1000 -0.0005
660 0.3673 nan 0.1000 -0.0009
680 0.3627 nan 0.1000 -0.0009
700 0.3551 nan 0.1000 -0.0010
720 0.3503 nan 0.1000 -0.0011
740 0.3454 nan 0.1000 -0.0010
760 0.3404 nan 0.1000 -0.0005
780 0.3356 nan 0.1000 -0.0013
800 0.3290 nan 0.1000 -0.0008
820 0.3236 nan 0.1000 -0.0003
840 0.3197 nan 0.1000 -0.0005
860 0.3150 nan 0.1000 -0.0006
880 0.3108 nan 0.1000 -0.0009
900 0.3075 nan 0.1000 -0.0009
920 0.3044 nan 0.1000 -0.0006
940 0.3007 nan 0.1000 -0.0008
960 0.2954 nan 0.1000 -0.0004
980 0.2922 nan 0.1000 -0.0014
1000 0.2878 nan 0.1000 -0.0014
1020 0.2824 nan 0.1000 -0.0015
1040 0.2785 nan 0.1000 -0.0008
1060 0.2736 nan 0.1000 -0.0008
1080 0.2709 nan 0.1000 -0.0006
1100 0.2681 nan 0.1000 -0.0008
- Fold03.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3251 nan 0.0100 0.0031
2 1.3193 nan 0.0100 0.0029
3 1.3128 nan 0.0100 0.0029
4 1.3069 nan 0.0100 0.0030
5 1.3009 nan 0.0100 0.0028
6 1.2952 nan 0.0100 0.0027
7 1.2896 nan 0.0100 0.0026
8 1.2842 nan 0.0100 0.0027
9 1.2792 nan 0.0100 0.0026
10 1.2740 nan 0.0100 0.0025
20 1.2270 nan 0.0100 0.0021
40 1.1558 nan 0.0100 0.0015
60 1.1062 nan 0.0100 0.0011
80 1.0664 nan 0.0100 0.0008
100 1.0357 nan 0.0100 0.0006
120 1.0100 nan 0.0100 0.0005
140 0.9882 nan 0.0100 0.0004
160 0.9699 nan 0.0100 0.0004
180 0.9537 nan 0.0100 0.0003
200 0.9394 nan 0.0100 0.0003
220 0.9261 nan 0.0100 0.0002
240 0.9144 nan 0.0100 0.0002
260 0.9034 nan 0.0100 0.0002
280 0.8946 nan 0.0100 0.0001
300 0.8863 nan 0.0100 0.0002
320 0.8791 nan 0.0100 0.0001
340 0.8721 nan 0.0100 0.0001
360 0.8653 nan 0.0100 0.0001
380 0.8599 nan 0.0100 0.0001
400 0.8545 nan 0.0100 0.0001
420 0.8494 nan 0.0100 -0.0001
440 0.8443 nan 0.0100 0.0000
460 0.8403 nan 0.0100 -0.0000
480 0.8363 nan 0.0100 -0.0000
500 0.8324 nan 0.0100 0.0000
520 0.8286 nan 0.0100 0.0000
540 0.8253 nan 0.0100 0.0000
560 0.8222 nan 0.0100 -0.0001
580 0.8188 nan 0.0100 0.0000
600 0.8157 nan 0.0100 -0.0001
620 0.8124 nan 0.0100 0.0000
640 0.8098 nan 0.0100 -0.0001
660 0.8068 nan 0.0100 0.0000
680 0.8038 nan 0.0100 -0.0000
700 0.8014 nan 0.0100 0.0000
720 0.7987 nan 0.0100 -0.0000
740 0.7965 nan 0.0100 -0.0000
760 0.7942 nan 0.0100 -0.0001
780 0.7918 nan 0.0100 0.0000
800 0.7894 nan 0.0100 -0.0000
820 0.7871 nan 0.0100 -0.0001
840 0.7852 nan 0.0100 0.0000
860 0.7831 nan 0.0100 -0.0000
880 0.7812 nan 0.0100 -0.0000
900 0.7793 nan 0.0100 -0.0000
920 0.7773 nan 0.0100 -0.0000
940 0.7755 nan 0.0100 0.0000
960 0.7738 nan 0.0100 -0.0000
980 0.7721 nan 0.0100 -0.0001
1000 0.7705 nan 0.0100 0.0000
1020 0.7689 nan 0.0100 -0.0001
1040 0.7673 nan 0.0100 -0.0000
1060 0.7660 nan 0.0100 -0.0001
1080 0.7645 nan 0.0100 -0.0000
1100 0.7630 nan 0.0100 -0.0000
- Fold04.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3230 nan 0.0100 0.0040
2 1.3155 nan 0.0100 0.0038
3 1.3078 nan 0.0100 0.0037
4 1.2998 nan 0.0100 0.0033
5 1.2931 nan 0.0100 0.0035
6 1.2860 nan 0.0100 0.0032
7 1.2789 nan 0.0100 0.0033
8 1.2723 nan 0.0100 0.0032
9 1.2652 nan 0.0100 0.0033
10 1.2587 nan 0.0100 0.0035
20 1.1968 nan 0.0100 0.0025
40 1.1038 nan 0.0100 0.0017
60 1.0373 nan 0.0100 0.0014
80 0.9864 nan 0.0100 0.0008
100 0.9480 nan 0.0100 0.0008
120 0.9175 nan 0.0100 0.0004
140 0.8932 nan 0.0100 0.0003
160 0.8748 nan 0.0100 0.0004
180 0.8592 nan 0.0100 0.0003
200 0.8459 nan 0.0100 0.0001
220 0.8344 nan 0.0100 -0.0000
240 0.8235 nan 0.0100 0.0000
260 0.8142 nan 0.0100 0.0001
280 0.8065 nan 0.0100 0.0002
300 0.7984 nan 0.0100 0.0001
320 0.7913 nan 0.0100 -0.0000
340 0.7840 nan 0.0100 0.0000
360 0.7780 nan 0.0100 -0.0000
380 0.7728 nan 0.0100 0.0001
400 0.7674 nan 0.0100 -0.0001
420 0.7629 nan 0.0100 -0.0000
440 0.7584 nan 0.0100 0.0000
460 0.7535 nan 0.0100 0.0000
480 0.7495 nan 0.0100 -0.0000
500 0.7456 nan 0.0100 -0.0001
520 0.7419 nan 0.0100 -0.0001
540 0.7380 nan 0.0100 -0.0001
560 0.7345 nan 0.0100 -0.0000
580 0.7309 nan 0.0100 -0.0001
600 0.7277 nan 0.0100 0.0000
620 0.7245 nan 0.0100 -0.0001
640 0.7215 nan 0.0100 0.0000
660 0.7190 nan 0.0100 -0.0000
680 0.7161 nan 0.0100 -0.0001
700 0.7132 nan 0.0100 -0.0001
720 0.7104 nan 0.0100 -0.0001
740 0.7076 nan 0.0100 -0.0001
760 0.7051 nan 0.0100 -0.0002
780 0.7026 nan 0.0100 -0.0001
800 0.7001 nan 0.0100 -0.0002
820 0.6979 nan 0.0100 -0.0001
840 0.6957 nan 0.0100 -0.0000
860 0.6934 nan 0.0100 0.0000
880 0.6915 nan 0.0100 -0.0001
900 0.6892 nan 0.0100 -0.0002
920 0.6870 nan 0.0100 -0.0000
940 0.6849 nan 0.0100 -0.0001
960 0.6828 nan 0.0100 -0.0001
980 0.6811 nan 0.0100 -0.0001
1000 0.6793 nan 0.0100 -0.0001
1020 0.6774 nan 0.0100 -0.0000
1040 0.6758 nan 0.0100 -0.0000
1060 0.6739 nan 0.0100 -0.0001
1080 0.6720 nan 0.0100 -0.0001
1100 0.6703 nan 0.0100 -0.0001
- Fold04.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3228 nan 0.0100 0.0043
2 1.3148 nan 0.0100 0.0042
3 1.3063 nan 0.0100 0.0039
4 1.2986 nan 0.0100 0.0038
5 1.2910 nan 0.0100 0.0041
6 1.2833 nan 0.0100 0.0039
7 1.2753 nan 0.0100 0.0037
8 1.2681 nan 0.0100 0.0038
9 1.2609 nan 0.0100 0.0037
10 1.2538 nan 0.0100 0.0036
20 1.1873 nan 0.0100 0.0027
40 1.0863 nan 0.0100 0.0021
60 1.0120 nan 0.0100 0.0015
80 0.9579 nan 0.0100 0.0011
100 0.9146 nan 0.0100 0.0009
120 0.8814 nan 0.0100 0.0005
140 0.8564 nan 0.0100 0.0002
160 0.8368 nan 0.0100 0.0003
180 0.8199 nan 0.0100 0.0004
200 0.8054 nan 0.0100 0.0001
220 0.7932 nan 0.0100 -0.0000
240 0.7827 nan 0.0100 -0.0000
260 0.7731 nan 0.0100 -0.0000
280 0.7640 nan 0.0100 0.0000
300 0.7565 nan 0.0100 0.0000
320 0.7489 nan 0.0100 -0.0001
340 0.7411 nan 0.0100 0.0000
360 0.7344 nan 0.0100 -0.0000
380 0.7280 nan 0.0100 -0.0000
400 0.7222 nan 0.0100 0.0000
420 0.7174 nan 0.0100 0.0000
440 0.7123 nan 0.0100 -0.0000
460 0.7073 nan 0.0100 -0.0000
480 0.7026 nan 0.0100 0.0000
500 0.6979 nan 0.0100 -0.0001
520 0.6936 nan 0.0100 -0.0000
540 0.6893 nan 0.0100 -0.0001
560 0.6860 nan 0.0100 -0.0001
580 0.6823 nan 0.0100 -0.0001
600 0.6783 nan 0.0100 -0.0001
620 0.6743 nan 0.0100 -0.0001
640 0.6709 nan 0.0100 0.0000
660 0.6674 nan 0.0100 -0.0000
680 0.6642 nan 0.0100 -0.0001
700 0.6617 nan 0.0100 -0.0002
720 0.6586 nan 0.0100 -0.0002
740 0.6555 nan 0.0100 -0.0000
760 0.6528 nan 0.0100 -0.0001
780 0.6499 nan 0.0100 -0.0001
800 0.6473 nan 0.0100 -0.0001
820 0.6445 nan 0.0100 -0.0001
840 0.6415 nan 0.0100 -0.0002
860 0.6388 nan 0.0100 -0.0001
880 0.6363 nan 0.0100 -0.0002
900 0.6337 nan 0.0100 -0.0001
920 0.6306 nan 0.0100 -0.0001
940 0.6285 nan 0.0100 -0.0001
960 0.6260 nan 0.0100 -0.0002
980 0.6237 nan 0.0100 -0.0002
1000 0.6214 nan 0.0100 -0.0002
1020 0.6192 nan 0.0100 -0.0001
1040 0.6169 nan 0.0100 -0.0002
1060 0.6143 nan 0.0100 -0.0001
1080 0.6122 nan 0.0100 -0.0002
1100 0.6098 nan 0.0100 -0.0002
- Fold04.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2742 nan 0.1000 0.0296
2 1.2307 nan 0.1000 0.0240
3 1.1896 nan 0.1000 0.0208
4 1.1573 nan 0.1000 0.0165
5 1.1289 nan 0.1000 0.0130
6 1.1013 nan 0.1000 0.0117
7 1.0816 nan 0.1000 0.0088
8 1.0652 nan 0.1000 0.0082
9 1.0475 nan 0.1000 0.0084
10 1.0326 nan 0.1000 0.0051
20 0.9352 nan 0.1000 0.0028
40 0.8505 nan 0.1000 0.0009
60 0.8139 nan 0.1000 -0.0005
80 0.7862 nan 0.1000 -0.0002
100 0.7691 nan 0.1000 -0.0002
120 0.7561 nan 0.1000 -0.0005
140 0.7459 nan 0.1000 -0.0001
160 0.7363 nan 0.1000 -0.0006
180 0.7283 nan 0.1000 -0.0016
200 0.7210 nan 0.1000 -0.0006
220 0.7138 nan 0.1000 -0.0005
240 0.7082 nan 0.1000 -0.0016
260 0.7016 nan 0.1000 -0.0017
280 0.6986 nan 0.1000 -0.0015
300 0.6937 nan 0.1000 -0.0010
320 0.6906 nan 0.1000 -0.0009
340 0.6860 nan 0.1000 -0.0007
360 0.6816 nan 0.1000 -0.0008
380 0.6788 nan 0.1000 -0.0008
400 0.6753 nan 0.1000 -0.0002
420 0.6724 nan 0.1000 -0.0007
440 0.6692 nan 0.1000 -0.0006
460 0.6671 nan 0.1000 -0.0026
480 0.6654 nan 0.1000 -0.0003
500 0.6634 nan 0.1000 -0.0003
520 0.6602 nan 0.1000 -0.0009
540 0.6579 nan 0.1000 -0.0007
560 0.6564 nan 0.1000 -0.0010
580 0.6552 nan 0.1000 -0.0006
600 0.6530 nan 0.1000 -0.0009
620 0.6511 nan 0.1000 -0.0004
640 0.6499 nan 0.1000 -0.0006
660 0.6493 nan 0.1000 -0.0013
680 0.6463 nan 0.1000 -0.0003
700 0.6446 nan 0.1000 -0.0004
720 0.6421 nan 0.1000 -0.0008
740 0.6416 nan 0.1000 -0.0005
760 0.6386 nan 0.1000 -0.0015
780 0.6372 nan 0.1000 -0.0010
800 0.6345 nan 0.1000 -0.0003
820 0.6329 nan 0.1000 -0.0011
840 0.6310 nan 0.1000 -0.0008
860 0.6291 nan 0.1000 -0.0013
880 0.6280 nan 0.1000 -0.0006
900 0.6260 nan 0.1000 -0.0013
920 0.6254 nan 0.1000 -0.0006
940 0.6224 nan 0.1000 -0.0007
960 0.6205 nan 0.1000 -0.0005
980 0.6185 nan 0.1000 -0.0002
1000 0.6170 nan 0.1000 -0.0004
1020 0.6162 nan 0.1000 -0.0006
1040 0.6146 nan 0.1000 -0.0009
1060 0.6132 nan 0.1000 -0.0007
1080 0.6125 nan 0.1000 -0.0008
1100 0.6102 nan 0.1000 -0.0004
- Fold04.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2528 nan 0.1000 0.0368
2 1.1946 nan 0.1000 0.0300
3 1.1427 nan 0.1000 0.0250
4 1.0969 nan 0.1000 0.0209
5 1.0613 nan 0.1000 0.0163
6 1.0319 nan 0.1000 0.0145
7 1.0051 nan 0.1000 0.0131
8 0.9811 nan 0.1000 0.0097
9 0.9633 nan 0.1000 0.0085
10 0.9462 nan 0.1000 0.0086
20 0.8440 nan 0.1000 0.0010
40 0.7617 nan 0.1000 0.0001
60 0.7217 nan 0.1000 -0.0002
80 0.6960 nan 0.1000 0.0002
100 0.6757 nan 0.1000 -0.0009
120 0.6596 nan 0.1000 -0.0012
140 0.6423 nan 0.1000 -0.0007
160 0.6298 nan 0.1000 -0.0025
180 0.6184 nan 0.1000 -0.0002
200 0.6079 nan 0.1000 -0.0009
220 0.6002 nan 0.1000 -0.0011
240 0.5888 nan 0.1000 -0.0005
260 0.5792 nan 0.1000 -0.0006
280 0.5701 nan 0.1000 -0.0014
300 0.5632 nan 0.1000 -0.0017
320 0.5560 nan 0.1000 -0.0020
340 0.5500 nan 0.1000 -0.0010
360 0.5434 nan 0.1000 -0.0020
380 0.5363 nan 0.1000 -0.0008
400 0.5286 nan 0.1000 -0.0022
420 0.5228 nan 0.1000 -0.0004
440 0.5148 nan 0.1000 -0.0010
460 0.5066 nan 0.1000 -0.0010
480 0.5000 nan 0.1000 -0.0006
500 0.4916 nan 0.1000 -0.0003
520 0.4859 nan 0.1000 -0.0006
540 0.4823 nan 0.1000 -0.0010
560 0.4767 nan 0.1000 -0.0002
580 0.4717 nan 0.1000 -0.0005
600 0.4672 nan 0.1000 -0.0005
620 0.4623 nan 0.1000 -0.0007
640 0.4574 nan 0.1000 -0.0016
660 0.4516 nan 0.1000 -0.0006
680 0.4483 nan 0.1000 -0.0010
700 0.4424 nan 0.1000 -0.0009
720 0.4399 nan 0.1000 -0.0013
740 0.4341 nan 0.1000 -0.0007
760 0.4300 nan 0.1000 -0.0008
780 0.4257 nan 0.1000 -0.0004
800 0.4228 nan 0.1000 -0.0009
820 0.4186 nan 0.1000 -0.0008
840 0.4153 nan 0.1000 -0.0010
860 0.4125 nan 0.1000 -0.0017
880 0.4094 nan 0.1000 -0.0008
900 0.4057 nan 0.1000 -0.0012
920 0.4021 nan 0.1000 -0.0006
940 0.3971 nan 0.1000 -0.0005
960 0.3946 nan 0.1000 -0.0014
980 0.3916 nan 0.1000 -0.0010
1000 0.3883 nan 0.1000 -0.0007
1020 0.3841 nan 0.1000 -0.0014
1040 0.3817 nan 0.1000 -0.0012
1060 0.3798 nan 0.1000 -0.0009
1080 0.3764 nan 0.1000 -0.0009
1100 0.3738 nan 0.1000 -0.0009
- Fold04.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2522 nan 0.1000 0.0420
2 1.1870 nan 0.1000 0.0332
3 1.1287 nan 0.1000 0.0275
4 1.0794 nan 0.1000 0.0230
5 1.0413 nan 0.1000 0.0185
6 1.0112 nan 0.1000 0.0120
7 0.9824 nan 0.1000 0.0149
8 0.9562 nan 0.1000 0.0111
9 0.9407 nan 0.1000 0.0065
10 0.9194 nan 0.1000 0.0086
20 0.8088 nan 0.1000 0.0020
40 0.7289 nan 0.1000 0.0003
60 0.6888 nan 0.1000 -0.0002
80 0.6569 nan 0.1000 -0.0006
100 0.6276 nan 0.1000 -0.0017
120 0.6024 nan 0.1000 -0.0007
140 0.5839 nan 0.1000 -0.0006
160 0.5637 nan 0.1000 -0.0008
180 0.5492 nan 0.1000 -0.0011
200 0.5340 nan 0.1000 -0.0008
220 0.5220 nan 0.1000 -0.0008
240 0.5075 nan 0.1000 -0.0008
260 0.4951 nan 0.1000 -0.0004
280 0.4819 nan 0.1000 -0.0012
300 0.4682 nan 0.1000 -0.0011
320 0.4566 nan 0.1000 -0.0009
340 0.4456 nan 0.1000 -0.0010
360 0.4374 nan 0.1000 -0.0014
380 0.4267 nan 0.1000 -0.0004
400 0.4161 nan 0.1000 -0.0016
420 0.4071 nan 0.1000 -0.0014
440 0.3975 nan 0.1000 -0.0006
460 0.3909 nan 0.1000 -0.0008
480 0.3836 nan 0.1000 -0.0012
500 0.3782 nan 0.1000 -0.0010
520 0.3728 nan 0.1000 -0.0015
540 0.3670 nan 0.1000 -0.0011
560 0.3595 nan 0.1000 -0.0008
580 0.3510 nan 0.1000 -0.0016
600 0.3444 nan 0.1000 -0.0008
620 0.3369 nan 0.1000 -0.0012
640 0.3310 nan 0.1000 -0.0008
660 0.3257 nan 0.1000 -0.0011
680 0.3202 nan 0.1000 -0.0012
700 0.3120 nan 0.1000 -0.0007
720 0.3071 nan 0.1000 -0.0005
740 0.3008 nan 0.1000 -0.0005
760 0.2963 nan 0.1000 -0.0005
780 0.2921 nan 0.1000 -0.0013
800 0.2866 nan 0.1000 -0.0009
820 0.2834 nan 0.1000 -0.0010
840 0.2787 nan 0.1000 -0.0012
860 0.2740 nan 0.1000 -0.0014
880 0.2696 nan 0.1000 -0.0005
900 0.2658 nan 0.1000 -0.0005
920 0.2617 nan 0.1000 -0.0010
940 0.2574 nan 0.1000 -0.0009
960 0.2531 nan 0.1000 -0.0005
980 0.2485 nan 0.1000 -0.0007
1000 0.2462 nan 0.1000 -0.0003
1020 0.2429 nan 0.1000 -0.0007
1040 0.2397 nan 0.1000 -0.0008
1060 0.2389 nan 0.1000 -0.0012
1080 0.2347 nan 0.1000 -0.0007
1100 0.2324 nan 0.1000 -0.0009
- Fold04.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3260 nan 0.0100 0.0030
2 1.3197 nan 0.0100 0.0029
3 1.3139 nan 0.0100 0.0029
4 1.3079 nan 0.0100 0.0028
5 1.3022 nan 0.0100 0.0028
6 1.2968 nan 0.0100 0.0028
7 1.2916 nan 0.0100 0.0026
8 1.2867 nan 0.0100 0.0026
9 1.2823 nan 0.0100 0.0025
10 1.2769 nan 0.0100 0.0025
20 1.2306 nan 0.0100 0.0021
40 1.1615 nan 0.0100 0.0014
60 1.1122 nan 0.0100 0.0011
80 1.0726 nan 0.0100 0.0008
100 1.0404 nan 0.0100 0.0006
120 1.0160 nan 0.0100 0.0005
140 0.9943 nan 0.0100 0.0003
160 0.9756 nan 0.0100 0.0003
180 0.9598 nan 0.0100 0.0003
200 0.9460 nan 0.0100 0.0003
220 0.9337 nan 0.0100 0.0001
240 0.9228 nan 0.0100 0.0001
260 0.9129 nan 0.0100 0.0002
280 0.9040 nan 0.0100 0.0001
300 0.8967 nan 0.0100 0.0001
320 0.8895 nan 0.0100 0.0002
340 0.8827 nan 0.0100 0.0001
360 0.8767 nan 0.0100 0.0000
380 0.8709 nan 0.0100 0.0000
400 0.8662 nan 0.0100 0.0001
420 0.8616 nan 0.0100 0.0001
440 0.8569 nan 0.0100 0.0001
460 0.8524 nan 0.0100 0.0001
480 0.8483 nan 0.0100 0.0000
500 0.8446 nan 0.0100 -0.0001
520 0.8408 nan 0.0100 0.0001
540 0.8371 nan 0.0100 0.0000
560 0.8336 nan 0.0100 -0.0000
580 0.8304 nan 0.0100 -0.0000
600 0.8273 nan 0.0100 0.0000
620 0.8247 nan 0.0100 -0.0001
640 0.8221 nan 0.0100 -0.0001
660 0.8195 nan 0.0100 0.0000
680 0.8169 nan 0.0100 -0.0000
700 0.8144 nan 0.0100 -0.0000
720 0.8120 nan 0.0100 -0.0000
740 0.8098 nan 0.0100 -0.0001
760 0.8077 nan 0.0100 0.0000
780 0.8055 nan 0.0100 0.0000
800 0.8037 nan 0.0100 -0.0000
820 0.8017 nan 0.0100 0.0000
840 0.7999 nan 0.0100 -0.0001
860 0.7981 nan 0.0100 -0.0000
880 0.7962 nan 0.0100 0.0000
900 0.7944 nan 0.0100 -0.0000
920 0.7926 nan 0.0100 -0.0000
940 0.7910 nan 0.0100 -0.0000
960 0.7893 nan 0.0100 -0.0000
980 0.7877 nan 0.0100 -0.0000
1000 0.7861 nan 0.0100 -0.0001
1020 0.7846 nan 0.0100 -0.0000
1040 0.7832 nan 0.0100 -0.0002
1060 0.7818 nan 0.0100 -0.0000
1080 0.7806 nan 0.0100 -0.0000
1100 0.7793 nan 0.0100 -0.0001
- Fold05.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3233 nan 0.0100 0.0039
2 1.3162 nan 0.0100 0.0036
3 1.3087 nan 0.0100 0.0035
4 1.3015 nan 0.0100 0.0036
5 1.2945 nan 0.0100 0.0035
6 1.2874 nan 0.0100 0.0033
7 1.2805 nan 0.0100 0.0036
8 1.2737 nan 0.0100 0.0032
9 1.2667 nan 0.0100 0.0033
10 1.2602 nan 0.0100 0.0032
20 1.2036 nan 0.0100 0.0026
40 1.1156 nan 0.0100 0.0016
60 1.0499 nan 0.0100 0.0014
80 0.9996 nan 0.0100 0.0009
100 0.9604 nan 0.0100 0.0008
120 0.9317 nan 0.0100 0.0006
140 0.9088 nan 0.0100 0.0003
160 0.8911 nan 0.0100 0.0004
180 0.8749 nan 0.0100 0.0003
200 0.8621 nan 0.0100 0.0001
220 0.8502 nan 0.0100 0.0001
240 0.8400 nan 0.0100 0.0001
260 0.8306 nan 0.0100 0.0001
280 0.8226 nan 0.0100 0.0000
300 0.8152 nan 0.0100 0.0001
320 0.8083 nan 0.0100 0.0001
340 0.8024 nan 0.0100 0.0000
360 0.7965 nan 0.0100 0.0000
380 0.7911 nan 0.0100 0.0001
400 0.7861 nan 0.0100 -0.0000
420 0.7816 nan 0.0100 -0.0000
440 0.7771 nan 0.0100 -0.0000
460 0.7730 nan 0.0100 -0.0000
480 0.7687 nan 0.0100 0.0000
500 0.7644 nan 0.0100 -0.0001
520 0.7611 nan 0.0100 -0.0001
540 0.7576 nan 0.0100 0.0000
560 0.7544 nan 0.0100 -0.0000
580 0.7510 nan 0.0100 -0.0001
600 0.7477 nan 0.0100 0.0000
620 0.7446 nan 0.0100 0.0000
640 0.7414 nan 0.0100 -0.0000
660 0.7383 nan 0.0100 -0.0000
680 0.7357 nan 0.0100 -0.0001
700 0.7327 nan 0.0100 -0.0000
720 0.7298 nan 0.0100 -0.0000
740 0.7274 nan 0.0100 -0.0001
760 0.7247 nan 0.0100 -0.0001
780 0.7218 nan 0.0100 -0.0001
800 0.7194 nan 0.0100 -0.0001
820 0.7173 nan 0.0100 -0.0000
840 0.7152 nan 0.0100 -0.0001
860 0.7130 nan 0.0100 -0.0001
880 0.7108 nan 0.0100 -0.0001
900 0.7083 nan 0.0100 -0.0001
920 0.7059 nan 0.0100 -0.0000
940 0.7040 nan 0.0100 -0.0001
960 0.7016 nan 0.0100 -0.0001
980 0.6996 nan 0.0100 -0.0001
1000 0.6976 nan 0.0100 -0.0001
1020 0.6955 nan 0.0100 0.0000
1040 0.6936 nan 0.0100 -0.0001
1060 0.6919 nan 0.0100 -0.0001
1080 0.6899 nan 0.0100 -0.0001
1100 0.6881 nan 0.0100 -0.0001
- Fold05.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3233 nan 0.0100 0.0040
2 1.3154 nan 0.0100 0.0040
3 1.3075 nan 0.0100 0.0037
4 1.2994 nan 0.0100 0.0037
5 1.2914 nan 0.0100 0.0037
6 1.2834 nan 0.0100 0.0035
7 1.2761 nan 0.0100 0.0036
8 1.2689 nan 0.0100 0.0036
9 1.2617 nan 0.0100 0.0033
10 1.2551 nan 0.0100 0.0034
20 1.1921 nan 0.0100 0.0029
40 1.0929 nan 0.0100 0.0020
60 1.0210 nan 0.0100 0.0016
80 0.9681 nan 0.0100 0.0011
100 0.9285 nan 0.0100 0.0007
120 0.8991 nan 0.0100 0.0007
140 0.8751 nan 0.0100 0.0005
160 0.8564 nan 0.0100 0.0003
180 0.8400 nan 0.0100 -0.0000
200 0.8262 nan 0.0100 0.0001
220 0.8134 nan 0.0100 0.0001
240 0.8025 nan 0.0100 0.0001
260 0.7928 nan 0.0100 -0.0000
280 0.7841 nan 0.0100 0.0001
300 0.7763 nan 0.0100 -0.0000
320 0.7695 nan 0.0100 0.0001
340 0.7625 nan 0.0100 -0.0000
360 0.7561 nan 0.0100 -0.0000
380 0.7499 nan 0.0100 0.0001
400 0.7445 nan 0.0100 -0.0002
420 0.7388 nan 0.0100 0.0001
440 0.7330 nan 0.0100 -0.0000
460 0.7279 nan 0.0100 0.0000
480 0.7232 nan 0.0100 -0.0001
500 0.7186 nan 0.0100 -0.0001
520 0.7149 nan 0.0100 -0.0001
540 0.7107 nan 0.0100 -0.0001
560 0.7071 nan 0.0100 -0.0002
580 0.7031 nan 0.0100 -0.0000
600 0.6990 nan 0.0100 -0.0001
620 0.6953 nan 0.0100 0.0001
640 0.6916 nan 0.0100 -0.0001
660 0.6885 nan 0.0100 -0.0002
680 0.6848 nan 0.0100 -0.0001
700 0.6811 nan 0.0100 -0.0001
720 0.6779 nan 0.0100 -0.0001
740 0.6749 nan 0.0100 -0.0001
760 0.6713 nan 0.0100 -0.0001
780 0.6683 nan 0.0100 -0.0000
800 0.6649 nan 0.0100 -0.0001
820 0.6616 nan 0.0100 -0.0001
840 0.6585 nan 0.0100 -0.0002
860 0.6557 nan 0.0100 -0.0001
880 0.6532 nan 0.0100 -0.0001
900 0.6504 nan 0.0100 -0.0000
920 0.6479 nan 0.0100 0.0000
940 0.6451 nan 0.0100 -0.0000
960 0.6421 nan 0.0100 -0.0001
980 0.6396 nan 0.0100 -0.0001
1000 0.6375 nan 0.0100 -0.0002
1020 0.6351 nan 0.0100 -0.0001
1040 0.6325 nan 0.0100 -0.0001
1060 0.6302 nan 0.0100 -0.0002
1080 0.6279 nan 0.0100 -0.0000
1100 0.6258 nan 0.0100 -0.0000
- Fold05.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2744 nan 0.1000 0.0289
2 1.2247 nan 0.1000 0.0230
3 1.1912 nan 0.1000 0.0189
4 1.1563 nan 0.1000 0.0141
5 1.1278 nan 0.1000 0.0138
6 1.1066 nan 0.1000 0.0083
7 1.0835 nan 0.1000 0.0108
8 1.0646 nan 0.1000 0.0085
9 1.0468 nan 0.1000 0.0068
10 1.0339 nan 0.1000 0.0059
20 0.9420 nan 0.1000 0.0023
40 0.8660 nan 0.1000 0.0008
60 0.8267 nan 0.1000 -0.0008
80 0.8019 nan 0.1000 -0.0004
100 0.7856 nan 0.1000 -0.0002
120 0.7711 nan 0.1000 -0.0002
140 0.7630 nan 0.1000 -0.0001
160 0.7552 nan 0.1000 -0.0005
180 0.7457 nan 0.1000 -0.0006
200 0.7385 nan 0.1000 -0.0002
220 0.7330 nan 0.1000 -0.0006
240 0.7273 nan 0.1000 -0.0001
260 0.7215 nan 0.1000 -0.0005
280 0.7172 nan 0.1000 -0.0001
300 0.7134 nan 0.1000 -0.0012
320 0.7088 nan 0.1000 -0.0007
340 0.7055 nan 0.1000 -0.0005
360 0.7011 nan 0.1000 -0.0010
380 0.6973 nan 0.1000 -0.0006
400 0.6950 nan 0.1000 -0.0007
420 0.6921 nan 0.1000 -0.0006
440 0.6896 nan 0.1000 -0.0013
460 0.6862 nan 0.1000 -0.0013
480 0.6846 nan 0.1000 -0.0010
500 0.6826 nan 0.1000 -0.0006
520 0.6802 nan 0.1000 -0.0010
540 0.6773 nan 0.1000 -0.0005
560 0.6761 nan 0.1000 -0.0006
580 0.6735 nan 0.1000 -0.0006
600 0.6725 nan 0.1000 -0.0014
620 0.6688 nan 0.1000 -0.0007
640 0.6675 nan 0.1000 -0.0012
660 0.6658 nan 0.1000 -0.0021
680 0.6629 nan 0.1000 -0.0004
700 0.6619 nan 0.1000 -0.0003
720 0.6590 nan 0.1000 -0.0006
740 0.6567 nan 0.1000 -0.0002
760 0.6549 nan 0.1000 -0.0003
780 0.6529 nan 0.1000 -0.0002
800 0.6517 nan 0.1000 -0.0011
820 0.6502 nan 0.1000 -0.0011
840 0.6471 nan 0.1000 -0.0010
860 0.6449 nan 0.1000 -0.0008
880 0.6436 nan 0.1000 -0.0009
900 0.6421 nan 0.1000 -0.0008
920 0.6409 nan 0.1000 -0.0013
940 0.6395 nan 0.1000 -0.0004
960 0.6368 nan 0.1000 -0.0007
980 0.6356 nan 0.1000 -0.0004
1000 0.6355 nan 0.1000 -0.0014
1020 0.6340 nan 0.1000 -0.0009
1040 0.6327 nan 0.1000 -0.0006
1060 0.6307 nan 0.1000 -0.0004
1080 0.6295 nan 0.1000 -0.0005
1100 0.6280 nan 0.1000 -0.0003
- Fold05.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2580 nan 0.1000 0.0336
2 1.1994 nan 0.1000 0.0303
3 1.1545 nan 0.1000 0.0242
4 1.1098 nan 0.1000 0.0209
5 1.0758 nan 0.1000 0.0153
6 1.0463 nan 0.1000 0.0129
7 1.0220 nan 0.1000 0.0121
8 1.0014 nan 0.1000 0.0106
9 0.9790 nan 0.1000 0.0096
10 0.9633 nan 0.1000 0.0087
20 0.8615 nan 0.1000 0.0019
40 0.7876 nan 0.1000 0.0004
60 0.7537 nan 0.1000 -0.0004
80 0.7249 nan 0.1000 -0.0012
100 0.7044 nan 0.1000 -0.0011
120 0.6842 nan 0.1000 -0.0012
140 0.6679 nan 0.1000 -0.0003
160 0.6527 nan 0.1000 -0.0009
180 0.6396 nan 0.1000 -0.0009
200 0.6295 nan 0.1000 -0.0007
220 0.6176 nan 0.1000 0.0003
240 0.6044 nan 0.1000 -0.0015
260 0.5957 nan 0.1000 -0.0009
280 0.5892 nan 0.1000 -0.0011
300 0.5795 nan 0.1000 -0.0012
320 0.5721 nan 0.1000 -0.0007
340 0.5627 nan 0.1000 -0.0012
360 0.5565 nan 0.1000 -0.0009
380 0.5458 nan 0.1000 -0.0013
400 0.5398 nan 0.1000 -0.0007
420 0.5320 nan 0.1000 -0.0013
440 0.5248 nan 0.1000 -0.0008
460 0.5175 nan 0.1000 -0.0008
480 0.5121 nan 0.1000 -0.0008
500 0.5046 nan 0.1000 -0.0005
520 0.5000 nan 0.1000 -0.0011
540 0.4944 nan 0.1000 -0.0014
560 0.4905 nan 0.1000 -0.0014
580 0.4839 nan 0.1000 -0.0009
600 0.4783 nan 0.1000 -0.0011
620 0.4734 nan 0.1000 -0.0019
640 0.4679 nan 0.1000 -0.0012
660 0.4630 nan 0.1000 -0.0007
680 0.4582 nan 0.1000 -0.0007
700 0.4516 nan 0.1000 -0.0003
720 0.4468 nan 0.1000 -0.0010
740 0.4421 nan 0.1000 -0.0005
760 0.4374 nan 0.1000 -0.0002
780 0.4336 nan 0.1000 -0.0006
800 0.4310 nan 0.1000 -0.0017
820 0.4292 nan 0.1000 -0.0009
840 0.4258 nan 0.1000 -0.0006
860 0.4229 nan 0.1000 -0.0017
880 0.4204 nan 0.1000 -0.0012
900 0.4165 nan 0.1000 -0.0012
920 0.4117 nan 0.1000 -0.0001
940 0.4104 nan 0.1000 -0.0006
960 0.4068 nan 0.1000 -0.0010
980 0.4047 nan 0.1000 -0.0011
1000 0.3999 nan 0.1000 -0.0006
1020 0.3969 nan 0.1000 -0.0011
1040 0.3933 nan 0.1000 -0.0009
1060 0.3909 nan 0.1000 -0.0010
1080 0.3884 nan 0.1000 -0.0009
1100 0.3853 nan 0.1000 -0.0008
- Fold05.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2505 nan 0.1000 0.0366
2 1.1835 nan 0.1000 0.0332
3 1.1309 nan 0.1000 0.0271
4 1.0855 nan 0.1000 0.0210
5 1.0453 nan 0.1000 0.0184
6 1.0119 nan 0.1000 0.0169
7 0.9829 nan 0.1000 0.0126
8 0.9557 nan 0.1000 0.0126
9 0.9340 nan 0.1000 0.0090
10 0.9185 nan 0.1000 0.0072
20 0.8228 nan 0.1000 0.0012
40 0.7523 nan 0.1000 -0.0006
60 0.7112 nan 0.1000 -0.0021
80 0.6750 nan 0.1000 -0.0009
100 0.6463 nan 0.1000 -0.0010
120 0.6243 nan 0.1000 -0.0007
140 0.5989 nan 0.1000 -0.0010
160 0.5798 nan 0.1000 -0.0011
180 0.5665 nan 0.1000 -0.0004
200 0.5481 nan 0.1000 -0.0006
220 0.5358 nan 0.1000 -0.0010
240 0.5240 nan 0.1000 -0.0011
260 0.5123 nan 0.1000 -0.0015
280 0.4993 nan 0.1000 -0.0008
300 0.4889 nan 0.1000 -0.0010
320 0.4800 nan 0.1000 -0.0017
340 0.4720 nan 0.1000 -0.0015
360 0.4621 nan 0.1000 -0.0005
380 0.4534 nan 0.1000 -0.0015
400 0.4463 nan 0.1000 -0.0011
420 0.4362 nan 0.1000 -0.0012
440 0.4273 nan 0.1000 -0.0010
460 0.4179 nan 0.1000 -0.0014
480 0.4105 nan 0.1000 -0.0007
500 0.4056 nan 0.1000 -0.0009
520 0.3978 nan 0.1000 -0.0014
540 0.3912 nan 0.1000 -0.0013
560 0.3834 nan 0.1000 -0.0014
580 0.3774 nan 0.1000 -0.0008
600 0.3707 nan 0.1000 -0.0014
620 0.3633 nan 0.1000 -0.0011
640 0.3591 nan 0.1000 -0.0012
660 0.3526 nan 0.1000 -0.0012
680 0.3482 nan 0.1000 -0.0012
700 0.3430 nan 0.1000 -0.0009
720 0.3385 nan 0.1000 -0.0011
740 0.3318 nan 0.1000 -0.0018
760 0.3268 nan 0.1000 -0.0010
780 0.3211 nan 0.1000 -0.0013
800 0.3165 nan 0.1000 -0.0009
820 0.3134 nan 0.1000 -0.0005
840 0.3095 nan 0.1000 -0.0010
860 0.3047 nan 0.1000 -0.0010
880 0.2997 nan 0.1000 -0.0013
900 0.2959 nan 0.1000 -0.0006
920 0.2921 nan 0.1000 -0.0005
940 0.2904 nan 0.1000 -0.0010
960 0.2875 nan 0.1000 -0.0007
980 0.2840 nan 0.1000 -0.0008
1000 0.2797 nan 0.1000 -0.0005
1020 0.2770 nan 0.1000 -0.0010
1040 0.2757 nan 0.1000 -0.0006
1060 0.2712 nan 0.1000 -0.0008
1080 0.2676 nan 0.1000 -0.0011
1100 0.2637 nan 0.1000 -0.0006
- Fold05.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3264 nan 0.0100 0.0028
2 1.3205 nan 0.0100 0.0028
3 1.3153 nan 0.0100 0.0027
4 1.3103 nan 0.0100 0.0027
5 1.3055 nan 0.0100 0.0026
6 1.3001 nan 0.0100 0.0026
7 1.2943 nan 0.0100 0.0025
8 1.2891 nan 0.0100 0.0025
9 1.2843 nan 0.0100 0.0024
10 1.2791 nan 0.0100 0.0023
20 1.2344 nan 0.0100 0.0019
40 1.1701 nan 0.0100 0.0014
60 1.1240 nan 0.0100 0.0010
80 1.0880 nan 0.0100 0.0006
100 1.0589 nan 0.0100 0.0006
120 1.0340 nan 0.0100 0.0003
140 1.0138 nan 0.0100 0.0004
160 0.9966 nan 0.0100 0.0003
180 0.9821 nan 0.0100 0.0003
200 0.9692 nan 0.0100 0.0002
220 0.9578 nan 0.0100 0.0002
240 0.9480 nan 0.0100 0.0002
260 0.9390 nan 0.0100 0.0001
280 0.9303 nan 0.0100 0.0002
300 0.9220 nan 0.0100 0.0002
320 0.9151 nan 0.0100 0.0001
340 0.9085 nan 0.0100 0.0000
360 0.9021 nan 0.0100 0.0001
380 0.8967 nan 0.0100 0.0001
400 0.8912 nan 0.0100 0.0001
420 0.8865 nan 0.0100 0.0001
440 0.8817 nan 0.0100 -0.0000
460 0.8774 nan 0.0100 0.0000
480 0.8731 nan 0.0100 0.0000
500 0.8695 nan 0.0100 0.0000
520 0.8655 nan 0.0100 0.0000
540 0.8616 nan 0.0100 0.0000
560 0.8579 nan 0.0100 -0.0000
580 0.8546 nan 0.0100 0.0000
600 0.8516 nan 0.0100 -0.0000
620 0.8490 nan 0.0100 -0.0000
640 0.8461 nan 0.0100 -0.0000
660 0.8432 nan 0.0100 -0.0000
680 0.8406 nan 0.0100 -0.0000
700 0.8378 nan 0.0100 -0.0000
720 0.8354 nan 0.0100 0.0000
740 0.8330 nan 0.0100 0.0000
760 0.8306 nan 0.0100 -0.0000
780 0.8283 nan 0.0100 -0.0000
800 0.8263 nan 0.0100 -0.0000
820 0.8243 nan 0.0100 0.0000
840 0.8223 nan 0.0100 -0.0000
860 0.8205 nan 0.0100 -0.0000
880 0.8186 nan 0.0100 -0.0000
900 0.8168 nan 0.0100 -0.0001
920 0.8152 nan 0.0100 -0.0000
940 0.8135 nan 0.0100 -0.0001
960 0.8119 nan 0.0100 -0.0000
980 0.8103 nan 0.0100 -0.0001
1000 0.8087 nan 0.0100 -0.0001
1020 0.8074 nan 0.0100 -0.0000
1040 0.8063 nan 0.0100 -0.0001
1060 0.8050 nan 0.0100 -0.0001
1080 0.8037 nan 0.0100 -0.0001
1100 0.8023 nan 0.0100 -0.0000
- Fold06.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3250 nan 0.0100 0.0035
2 1.3174 nan 0.0100 0.0034
3 1.3102 nan 0.0100 0.0034
4 1.3038 nan 0.0100 0.0033
5 1.2975 nan 0.0100 0.0033
6 1.2910 nan 0.0100 0.0032
7 1.2843 nan 0.0100 0.0031
8 1.2779 nan 0.0100 0.0030
9 1.2715 nan 0.0100 0.0027
10 1.2656 nan 0.0100 0.0030
20 1.2118 nan 0.0100 0.0025
40 1.1272 nan 0.0100 0.0016
60 1.0650 nan 0.0100 0.0013
80 1.0183 nan 0.0100 0.0009
100 0.9835 nan 0.0100 0.0007
120 0.9563 nan 0.0100 0.0005
140 0.9341 nan 0.0100 0.0003
160 0.9164 nan 0.0100 0.0004
180 0.9003 nan 0.0100 0.0004
200 0.8870 nan 0.0100 0.0002
220 0.8749 nan 0.0100 0.0001
240 0.8643 nan 0.0100 0.0001
260 0.8553 nan 0.0100 0.0000
280 0.8470 nan 0.0100 0.0000
300 0.8393 nan 0.0100 0.0001
320 0.8319 nan 0.0100 0.0000
340 0.8256 nan 0.0100 -0.0000
360 0.8193 nan 0.0100 0.0001
380 0.8133 nan 0.0100 0.0001
400 0.8086 nan 0.0100 0.0001
420 0.8037 nan 0.0100 -0.0000
440 0.7993 nan 0.0100 -0.0000
460 0.7947 nan 0.0100 -0.0001
480 0.7911 nan 0.0100 -0.0000
500 0.7872 nan 0.0100 -0.0001
520 0.7833 nan 0.0100 0.0000
540 0.7798 nan 0.0100 -0.0001
560 0.7767 nan 0.0100 -0.0001
580 0.7736 nan 0.0100 -0.0000
600 0.7702 nan 0.0100 -0.0001
620 0.7669 nan 0.0100 -0.0000
640 0.7641 nan 0.0100 -0.0001
660 0.7611 nan 0.0100 -0.0001
680 0.7584 nan 0.0100 -0.0000
700 0.7553 nan 0.0100 -0.0001
720 0.7528 nan 0.0100 -0.0001
740 0.7507 nan 0.0100 -0.0001
760 0.7484 nan 0.0100 -0.0000
780 0.7462 nan 0.0100 -0.0001
800 0.7438 nan 0.0100 -0.0001
820 0.7413 nan 0.0100 -0.0000
840 0.7395 nan 0.0100 -0.0001
860 0.7372 nan 0.0100 -0.0001
880 0.7350 nan 0.0100 -0.0002
900 0.7327 nan 0.0100 -0.0000
920 0.7302 nan 0.0100 -0.0000
940 0.7284 nan 0.0100 -0.0001
960 0.7261 nan 0.0100 -0.0001
980 0.7244 nan 0.0100 -0.0002
1000 0.7224 nan 0.0100 0.0000
1020 0.7205 nan 0.0100 -0.0000
1040 0.7185 nan 0.0100 -0.0002
1060 0.7165 nan 0.0100 -0.0000
1080 0.7148 nan 0.0100 -0.0001
1100 0.7129 nan 0.0100 -0.0001
- Fold06.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3239 nan 0.0100 0.0039
2 1.3162 nan 0.0100 0.0037
3 1.3081 nan 0.0100 0.0037
4 1.3007 nan 0.0100 0.0035
5 1.2935 nan 0.0100 0.0034
6 1.2867 nan 0.0100 0.0035
7 1.2796 nan 0.0100 0.0031
8 1.2726 nan 0.0100 0.0035
9 1.2660 nan 0.0100 0.0031
10 1.2594 nan 0.0100 0.0031
20 1.2001 nan 0.0100 0.0027
40 1.1066 nan 0.0100 0.0018
60 1.0379 nan 0.0100 0.0013
80 0.9873 nan 0.0100 0.0010
100 0.9514 nan 0.0100 0.0003
120 0.9220 nan 0.0100 0.0004
140 0.8989 nan 0.0100 0.0003
160 0.8780 nan 0.0100 0.0002
180 0.8614 nan 0.0100 0.0002
200 0.8472 nan 0.0100 0.0001
220 0.8358 nan 0.0100 0.0001
240 0.8235 nan 0.0100 0.0001
260 0.8138 nan 0.0100 0.0001
280 0.8050 nan 0.0100 0.0001
300 0.7967 nan 0.0100 0.0001
320 0.7895 nan 0.0100 0.0000
340 0.7823 nan 0.0100 -0.0001
360 0.7764 nan 0.0100 -0.0001
380 0.7707 nan 0.0100 0.0000
400 0.7650 nan 0.0100 0.0000
420 0.7595 nan 0.0100 -0.0001
440 0.7543 nan 0.0100 -0.0000
460 0.7502 nan 0.0100 -0.0002
480 0.7458 nan 0.0100 -0.0002
500 0.7413 nan 0.0100 -0.0001
520 0.7370 nan 0.0100 -0.0002
540 0.7331 nan 0.0100 -0.0000
560 0.7293 nan 0.0100 -0.0001
580 0.7258 nan 0.0100 -0.0001
600 0.7228 nan 0.0100 -0.0001
620 0.7189 nan 0.0100 -0.0000
640 0.7159 nan 0.0100 -0.0001
660 0.7124 nan 0.0100 -0.0002
680 0.7091 nan 0.0100 -0.0001
700 0.7060 nan 0.0100 -0.0002
720 0.7031 nan 0.0100 -0.0002
740 0.6997 nan 0.0100 -0.0000
760 0.6967 nan 0.0100 -0.0001
780 0.6930 nan 0.0100 -0.0001
800 0.6896 nan 0.0100 -0.0000
820 0.6867 nan 0.0100 -0.0001
840 0.6841 nan 0.0100 -0.0000
860 0.6816 nan 0.0100 -0.0002
880 0.6788 nan 0.0100 -0.0001
900 0.6766 nan 0.0100 -0.0000
920 0.6742 nan 0.0100 0.0000
940 0.6714 nan 0.0100 -0.0002
960 0.6689 nan 0.0100 -0.0002
980 0.6664 nan 0.0100 -0.0001
1000 0.6635 nan 0.0100 -0.0001
1020 0.6609 nan 0.0100 -0.0002
1040 0.6583 nan 0.0100 -0.0001
1060 0.6555 nan 0.0100 -0.0001
1080 0.6527 nan 0.0100 -0.0002
1100 0.6499 nan 0.0100 -0.0002
- Fold06.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2786 nan 0.1000 0.0260
2 1.2340 nan 0.1000 0.0216
3 1.1962 nan 0.1000 0.0191
4 1.1676 nan 0.1000 0.0140
5 1.1409 nan 0.1000 0.0131
6 1.1202 nan 0.1000 0.0107
7 1.1030 nan 0.1000 0.0079
8 1.0863 nan 0.1000 0.0072
9 1.0702 nan 0.1000 0.0071
10 1.0568 nan 0.1000 0.0050
20 0.9669 nan 0.1000 0.0026
40 0.8903 nan 0.1000 0.0005
60 0.8540 nan 0.1000 -0.0001
80 0.8267 nan 0.1000 -0.0007
100 0.8103 nan 0.1000 -0.0003
120 0.7999 nan 0.1000 -0.0012
140 0.7896 nan 0.1000 -0.0004
160 0.7826 nan 0.1000 -0.0012
180 0.7740 nan 0.1000 -0.0011
200 0.7678 nan 0.1000 -0.0008
220 0.7618 nan 0.1000 -0.0005
240 0.7569 nan 0.1000 -0.0005
260 0.7534 nan 0.1000 -0.0009
280 0.7500 nan 0.1000 -0.0002
300 0.7457 nan 0.1000 -0.0018
320 0.7407 nan 0.1000 -0.0005
340 0.7384 nan 0.1000 -0.0007
360 0.7353 nan 0.1000 -0.0011
380 0.7325 nan 0.1000 -0.0010
400 0.7293 nan 0.1000 -0.0001
420 0.7258 nan 0.1000 -0.0005
440 0.7217 nan 0.1000 -0.0007
460 0.7191 nan 0.1000 -0.0006
480 0.7171 nan 0.1000 -0.0004
500 0.7164 nan 0.1000 -0.0008
520 0.7133 nan 0.1000 -0.0020
540 0.7115 nan 0.1000 -0.0012
560 0.7090 nan 0.1000 -0.0005
580 0.7065 nan 0.1000 -0.0008
600 0.7044 nan 0.1000 -0.0005
620 0.7027 nan 0.1000 -0.0005
640 0.7007 nan 0.1000 -0.0001
660 0.6989 nan 0.1000 -0.0008
680 0.6976 nan 0.1000 -0.0008
700 0.6966 nan 0.1000 -0.0007
720 0.6943 nan 0.1000 -0.0008
740 0.6922 nan 0.1000 -0.0010
760 0.6910 nan 0.1000 -0.0007
780 0.6895 nan 0.1000 -0.0005
800 0.6873 nan 0.1000 -0.0008
820 0.6866 nan 0.1000 -0.0004
840 0.6834 nan 0.1000 -0.0003
860 0.6836 nan 0.1000 -0.0011
880 0.6825 nan 0.1000 -0.0020
900 0.6810 nan 0.1000 -0.0007
920 0.6792 nan 0.1000 -0.0008
940 0.6780 nan 0.1000 -0.0009
960 0.6773 nan 0.1000 -0.0006
980 0.6760 nan 0.1000 -0.0009
1000 0.6742 nan 0.1000 -0.0009
1020 0.6734 nan 0.1000 -0.0003
1040 0.6702 nan 0.1000 -0.0007
1060 0.6702 nan 0.1000 -0.0007
1080 0.6677 nan 0.1000 -0.0007
1100 0.6672 nan 0.1000 -0.0007
- Fold06.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2644 nan 0.1000 0.0339
2 1.2102 nan 0.1000 0.0275
3 1.1648 nan 0.1000 0.0220
4 1.1233 nan 0.1000 0.0196
5 1.0898 nan 0.1000 0.0163
6 1.0616 nan 0.1000 0.0138
7 1.0357 nan 0.1000 0.0116
8 1.0130 nan 0.1000 0.0095
9 0.9941 nan 0.1000 0.0077
10 0.9762 nan 0.1000 0.0070
20 0.8852 nan 0.1000 0.0016
40 0.8129 nan 0.1000 -0.0001
60 0.7755 nan 0.1000 -0.0001
80 0.7467 nan 0.1000 -0.0014
100 0.7320 nan 0.1000 -0.0017
120 0.7137 nan 0.1000 -0.0011
140 0.6938 nan 0.1000 -0.0008
160 0.6827 nan 0.1000 -0.0020
180 0.6729 nan 0.1000 -0.0010
200 0.6624 nan 0.1000 -0.0006
220 0.6516 nan 0.1000 -0.0010
240 0.6376 nan 0.1000 -0.0001
260 0.6253 nan 0.1000 -0.0015
280 0.6157 nan 0.1000 -0.0011
300 0.6041 nan 0.1000 -0.0005
320 0.5961 nan 0.1000 -0.0008
340 0.5881 nan 0.1000 -0.0010
360 0.5794 nan 0.1000 -0.0009
380 0.5713 nan 0.1000 -0.0010
400 0.5655 nan 0.1000 -0.0008
420 0.5572 nan 0.1000 -0.0006
440 0.5501 nan 0.1000 -0.0011
460 0.5433 nan 0.1000 -0.0010
480 0.5359 nan 0.1000 -0.0008
500 0.5301 nan 0.1000 -0.0016
520 0.5233 nan 0.1000 -0.0014
540 0.5177 nan 0.1000 -0.0006
560 0.5136 nan 0.1000 -0.0009
580 0.5074 nan 0.1000 -0.0006
600 0.5019 nan 0.1000 -0.0012
620 0.4966 nan 0.1000 -0.0011
640 0.4918 nan 0.1000 -0.0005
660 0.4878 nan 0.1000 -0.0010
680 0.4821 nan 0.1000 -0.0005
700 0.4785 nan 0.1000 -0.0012
720 0.4742 nan 0.1000 -0.0017
740 0.4696 nan 0.1000 -0.0009
760 0.4643 nan 0.1000 -0.0009
780 0.4615 nan 0.1000 -0.0011
800 0.4571 nan 0.1000 -0.0015
820 0.4532 nan 0.1000 -0.0008
840 0.4484 nan 0.1000 -0.0009
860 0.4442 nan 0.1000 -0.0005
880 0.4414 nan 0.1000 -0.0009
900 0.4392 nan 0.1000 -0.0017
920 0.4350 nan 0.1000 -0.0004
940 0.4315 nan 0.1000 -0.0006
960 0.4273 nan 0.1000 -0.0011
980 0.4247 nan 0.1000 -0.0013
1000 0.4209 nan 0.1000 -0.0007
1020 0.4166 nan 0.1000 -0.0010
1040 0.4136 nan 0.1000 -0.0008
1060 0.4103 nan 0.1000 -0.0006
1080 0.4071 nan 0.1000 -0.0009
1100 0.4046 nan 0.1000 -0.0017
- Fold06.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2568 nan 0.1000 0.0386
2 1.1981 nan 0.1000 0.0266
3 1.1487 nan 0.1000 0.0246
4 1.1067 nan 0.1000 0.0211
5 1.0711 nan 0.1000 0.0176
6 1.0372 nan 0.1000 0.0150
7 1.0095 nan 0.1000 0.0134
8 0.9838 nan 0.1000 0.0103
9 0.9640 nan 0.1000 0.0087
10 0.9436 nan 0.1000 0.0071
20 0.8505 nan 0.1000 0.0013
40 0.7725 nan 0.1000 -0.0000
60 0.7307 nan 0.1000 -0.0006
80 0.6984 nan 0.1000 -0.0009
100 0.6731 nan 0.1000 -0.0010
120 0.6494 nan 0.1000 -0.0008
140 0.6267 nan 0.1000 -0.0019
160 0.6036 nan 0.1000 -0.0014
180 0.5850 nan 0.1000 -0.0013
200 0.5698 nan 0.1000 -0.0016
220 0.5518 nan 0.1000 -0.0016
240 0.5365 nan 0.1000 -0.0016
260 0.5266 nan 0.1000 -0.0002
280 0.5134 nan 0.1000 -0.0008
300 0.5020 nan 0.1000 -0.0006
320 0.4904 nan 0.1000 -0.0011
340 0.4793 nan 0.1000 -0.0010
360 0.4686 nan 0.1000 -0.0011
380 0.4594 nan 0.1000 -0.0014
400 0.4507 nan 0.1000 -0.0010
420 0.4425 nan 0.1000 -0.0008
440 0.4350 nan 0.1000 -0.0014
460 0.4259 nan 0.1000 -0.0010
480 0.4186 nan 0.1000 -0.0011
500 0.4126 nan 0.1000 -0.0009
520 0.4036 nan 0.1000 -0.0014
540 0.3973 nan 0.1000 -0.0015
560 0.3906 nan 0.1000 -0.0008
580 0.3827 nan 0.1000 -0.0009
600 0.3770 nan 0.1000 -0.0013
620 0.3729 nan 0.1000 -0.0012
640 0.3658 nan 0.1000 -0.0011
660 0.3604 nan 0.1000 -0.0007
680 0.3543 nan 0.1000 -0.0009
700 0.3493 nan 0.1000 -0.0009
720 0.3447 nan 0.1000 -0.0007
740 0.3404 nan 0.1000 -0.0012
760 0.3357 nan 0.1000 -0.0009
780 0.3310 nan 0.1000 -0.0012
800 0.3265 nan 0.1000 -0.0008
820 0.3212 nan 0.1000 -0.0010
840 0.3171 nan 0.1000 -0.0010
860 0.3135 nan 0.1000 -0.0010
880 0.3097 nan 0.1000 -0.0015
900 0.3050 nan 0.1000 -0.0013
920 0.3010 nan 0.1000 -0.0015
940 0.2983 nan 0.1000 -0.0011
960 0.2950 nan 0.1000 -0.0005
980 0.2905 nan 0.1000 -0.0006
1000 0.2862 nan 0.1000 -0.0008
1020 0.2825 nan 0.1000 -0.0007
1040 0.2794 nan 0.1000 -0.0012
1060 0.2768 nan 0.1000 -0.0008
1080 0.2740 nan 0.1000 -0.0011
1100 0.2705 nan 0.1000 -0.0008
- Fold06.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3259 nan 0.0100 0.0031
2 1.3203 nan 0.0100 0.0030
3 1.3141 nan 0.0100 0.0029
4 1.3080 nan 0.0100 0.0029
5 1.3027 nan 0.0100 0.0028
6 1.2971 nan 0.0100 0.0028
7 1.2910 nan 0.0100 0.0028
8 1.2857 nan 0.0100 0.0027
9 1.2801 nan 0.0100 0.0026
10 1.2749 nan 0.0100 0.0026
20 1.2280 nan 0.0100 0.0021
40 1.1556 nan 0.0100 0.0016
60 1.1063 nan 0.0100 0.0011
80 1.0664 nan 0.0100 0.0009
100 1.0351 nan 0.0100 0.0005
120 1.0085 nan 0.0100 0.0005
140 0.9870 nan 0.0100 0.0004
160 0.9679 nan 0.0100 0.0003
180 0.9520 nan 0.0100 0.0003
200 0.9379 nan 0.0100 0.0002
220 0.9256 nan 0.0100 0.0003
240 0.9144 nan 0.0100 0.0002
260 0.9043 nan 0.0100 0.0002
280 0.8953 nan 0.0100 0.0001
300 0.8869 nan 0.0100 0.0000
320 0.8794 nan 0.0100 -0.0000
340 0.8723 nan 0.0100 0.0001
360 0.8655 nan 0.0100 0.0001
380 0.8600 nan 0.0100 0.0001
400 0.8545 nan 0.0100 0.0000
420 0.8493 nan 0.0100 0.0000
440 0.8440 nan 0.0100 -0.0000
460 0.8391 nan 0.0100 -0.0001
480 0.8345 nan 0.0100 0.0000
500 0.8301 nan 0.0100 0.0000
520 0.8259 nan 0.0100 -0.0000
540 0.8224 nan 0.0100 0.0000
560 0.8189 nan 0.0100 0.0000
580 0.8154 nan 0.0100 -0.0001
600 0.8121 nan 0.0100 0.0000
620 0.8088 nan 0.0100 0.0000
640 0.8058 nan 0.0100 0.0000
660 0.8028 nan 0.0100 -0.0001
680 0.7999 nan 0.0100 0.0000
700 0.7971 nan 0.0100 -0.0000
720 0.7947 nan 0.0100 -0.0000
740 0.7923 nan 0.0100 -0.0000
760 0.7899 nan 0.0100 -0.0000
780 0.7879 nan 0.0100 -0.0000
800 0.7857 nan 0.0100 -0.0000
820 0.7836 nan 0.0100 -0.0001
840 0.7816 nan 0.0100 -0.0001
860 0.7797 nan 0.0100 -0.0001
880 0.7777 nan 0.0100 0.0000
900 0.7761 nan 0.0100 -0.0000
920 0.7743 nan 0.0100 -0.0000
940 0.7725 nan 0.0100 -0.0000
960 0.7708 nan 0.0100 -0.0000
980 0.7693 nan 0.0100 -0.0001
1000 0.7679 nan 0.0100 -0.0000
1020 0.7667 nan 0.0100 -0.0001
1040 0.7651 nan 0.0100 0.0000
1060 0.7635 nan 0.0100 0.0000
1080 0.7619 nan 0.0100 -0.0001
1100 0.7603 nan 0.0100 -0.0000
- Fold07.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3237 nan 0.0100 0.0037
2 1.3162 nan 0.0100 0.0034
3 1.3087 nan 0.0100 0.0037
4 1.3016 nan 0.0100 0.0035
5 1.2941 nan 0.0100 0.0036
6 1.2877 nan 0.0100 0.0034
7 1.2813 nan 0.0100 0.0035
8 1.2745 nan 0.0100 0.0034
9 1.2678 nan 0.0100 0.0031
10 1.2614 nan 0.0100 0.0030
20 1.2015 nan 0.0100 0.0028
40 1.1101 nan 0.0100 0.0017
60 1.0438 nan 0.0100 0.0014
80 0.9931 nan 0.0100 0.0010
100 0.9541 nan 0.0100 0.0008
120 0.9245 nan 0.0100 0.0004
140 0.8988 nan 0.0100 0.0004
160 0.8791 nan 0.0100 0.0003
180 0.8622 nan 0.0100 0.0003
200 0.8479 nan 0.0100 0.0003
220 0.8365 nan 0.0100 0.0001
240 0.8257 nan 0.0100 0.0001
260 0.8154 nan 0.0100 0.0001
280 0.8065 nan 0.0100 0.0001
300 0.7988 nan 0.0100 0.0000
320 0.7914 nan 0.0100 -0.0001
340 0.7850 nan 0.0100 0.0001
360 0.7785 nan 0.0100 -0.0000
380 0.7730 nan 0.0100 -0.0000
400 0.7675 nan 0.0100 0.0001
420 0.7623 nan 0.0100 -0.0000
440 0.7574 nan 0.0100 -0.0001
460 0.7528 nan 0.0100 -0.0000
480 0.7487 nan 0.0100 -0.0001
500 0.7447 nan 0.0100 -0.0002
520 0.7406 nan 0.0100 -0.0000
540 0.7372 nan 0.0100 0.0001
560 0.7336 nan 0.0100 0.0000
580 0.7298 nan 0.0100 -0.0001
600 0.7263 nan 0.0100 -0.0000
620 0.7230 nan 0.0100 -0.0001
640 0.7195 nan 0.0100 -0.0000
660 0.7168 nan 0.0100 -0.0001
680 0.7143 nan 0.0100 -0.0000
700 0.7115 nan 0.0100 -0.0001
720 0.7089 nan 0.0100 -0.0000
740 0.7066 nan 0.0100 -0.0001
760 0.7042 nan 0.0100 -0.0001
780 0.7020 nan 0.0100 -0.0001
800 0.6996 nan 0.0100 -0.0002
820 0.6976 nan 0.0100 -0.0000
840 0.6950 nan 0.0100 -0.0001
860 0.6930 nan 0.0100 -0.0000
880 0.6908 nan 0.0100 -0.0000
900 0.6886 nan 0.0100 -0.0000
920 0.6864 nan 0.0100 -0.0001
940 0.6847 nan 0.0100 -0.0001
960 0.6827 nan 0.0100 -0.0002
980 0.6808 nan 0.0100 -0.0001
1000 0.6788 nan 0.0100 -0.0001
1020 0.6767 nan 0.0100 -0.0001
1040 0.6750 nan 0.0100 -0.0000
1060 0.6735 nan 0.0100 -0.0001
1080 0.6715 nan 0.0100 -0.0001
1100 0.6695 nan 0.0100 -0.0001
- Fold07.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3234 nan 0.0100 0.0040
2 1.3149 nan 0.0100 0.0045
3 1.3070 nan 0.0100 0.0038
4 1.2992 nan 0.0100 0.0036
5 1.2914 nan 0.0100 0.0038
6 1.2834 nan 0.0100 0.0039
7 1.2756 nan 0.0100 0.0038
8 1.2686 nan 0.0100 0.0037
9 1.2611 nan 0.0100 0.0035
10 1.2534 nan 0.0100 0.0034
20 1.1886 nan 0.0100 0.0030
40 1.0869 nan 0.0100 0.0021
60 1.0143 nan 0.0100 0.0013
80 0.9587 nan 0.0100 0.0011
100 0.9179 nan 0.0100 0.0008
120 0.8864 nan 0.0100 0.0005
140 0.8609 nan 0.0100 0.0005
160 0.8414 nan 0.0100 0.0003
180 0.8249 nan 0.0100 0.0003
200 0.8109 nan 0.0100 0.0002
220 0.7981 nan 0.0100 -0.0000
240 0.7868 nan 0.0100 0.0002
260 0.7775 nan 0.0100 -0.0001
280 0.7689 nan 0.0100 -0.0001
300 0.7612 nan 0.0100 -0.0001
320 0.7540 nan 0.0100 -0.0000
340 0.7470 nan 0.0100 -0.0000
360 0.7402 nan 0.0100 -0.0000
380 0.7343 nan 0.0100 -0.0002
400 0.7289 nan 0.0100 -0.0002
420 0.7228 nan 0.0100 0.0001
440 0.7172 nan 0.0100 -0.0000
460 0.7119 nan 0.0100 -0.0001
480 0.7071 nan 0.0100 0.0000
500 0.7030 nan 0.0100 -0.0001
520 0.6987 nan 0.0100 -0.0001
540 0.6949 nan 0.0100 -0.0001
560 0.6908 nan 0.0100 -0.0000
580 0.6870 nan 0.0100 -0.0001
600 0.6836 nan 0.0100 -0.0001
620 0.6801 nan 0.0100 -0.0002
640 0.6770 nan 0.0100 -0.0001
660 0.6737 nan 0.0100 -0.0001
680 0.6705 nan 0.0100 -0.0002
700 0.6671 nan 0.0100 -0.0001
720 0.6643 nan 0.0100 -0.0000
740 0.6614 nan 0.0100 -0.0001
760 0.6582 nan 0.0100 -0.0001
780 0.6551 nan 0.0100 -0.0002
800 0.6524 nan 0.0100 -0.0003
820 0.6494 nan 0.0100 -0.0001
840 0.6461 nan 0.0100 -0.0001
860 0.6433 nan 0.0100 -0.0001
880 0.6406 nan 0.0100 -0.0001
900 0.6382 nan 0.0100 -0.0001
920 0.6352 nan 0.0100 -0.0001
940 0.6330 nan 0.0100 -0.0002
960 0.6308 nan 0.0100 -0.0002
980 0.6289 nan 0.0100 -0.0001
1000 0.6263 nan 0.0100 -0.0001
1020 0.6242 nan 0.0100 -0.0002
1040 0.6217 nan 0.0100 -0.0001
1060 0.6190 nan 0.0100 -0.0001
1080 0.6162 nan 0.0100 -0.0001
1100 0.6139 nan 0.0100 -0.0001
- Fold07.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2666 nan 0.1000 0.0291
2 1.2229 nan 0.1000 0.0242
3 1.1829 nan 0.1000 0.0198
4 1.1525 nan 0.1000 0.0166
5 1.1244 nan 0.1000 0.0135
6 1.1008 nan 0.1000 0.0114
7 1.0806 nan 0.1000 0.0091
8 1.0609 nan 0.1000 0.0080
9 1.0461 nan 0.1000 0.0067
10 1.0334 nan 0.1000 0.0055
20 0.9362 nan 0.1000 0.0023
40 0.8529 nan 0.1000 0.0003
60 0.8116 nan 0.1000 0.0000
80 0.7867 nan 0.1000 -0.0005
100 0.7690 nan 0.1000 -0.0008
120 0.7569 nan 0.1000 -0.0007
140 0.7461 nan 0.1000 -0.0008
160 0.7376 nan 0.1000 -0.0006
180 0.7304 nan 0.1000 -0.0006
200 0.7239 nan 0.1000 -0.0011
220 0.7177 nan 0.1000 -0.0004
240 0.7111 nan 0.1000 -0.0004
260 0.7065 nan 0.1000 -0.0005
280 0.7030 nan 0.1000 -0.0010
300 0.6989 nan 0.1000 -0.0015
320 0.6937 nan 0.1000 -0.0005
340 0.6897 nan 0.1000 -0.0000
360 0.6871 nan 0.1000 -0.0005
380 0.6836 nan 0.1000 -0.0004
400 0.6818 nan 0.1000 -0.0009
420 0.6782 nan 0.1000 -0.0010
440 0.6759 nan 0.1000 -0.0003
460 0.6741 nan 0.1000 -0.0007
480 0.6708 nan 0.1000 -0.0002
500 0.6693 nan 0.1000 -0.0012
520 0.6671 nan 0.1000 -0.0011
540 0.6646 nan 0.1000 -0.0011
560 0.6626 nan 0.1000 -0.0006
580 0.6606 nan 0.1000 -0.0013
600 0.6583 nan 0.1000 -0.0009
620 0.6573 nan 0.1000 -0.0009
640 0.6538 nan 0.1000 -0.0006
660 0.6520 nan 0.1000 -0.0003
680 0.6503 nan 0.1000 -0.0001
700 0.6491 nan 0.1000 -0.0004
720 0.6462 nan 0.1000 -0.0009
740 0.6438 nan 0.1000 -0.0005
760 0.6414 nan 0.1000 -0.0004
780 0.6404 nan 0.1000 -0.0014
800 0.6389 nan 0.1000 -0.0005
820 0.6372 nan 0.1000 -0.0007
840 0.6354 nan 0.1000 -0.0001
860 0.6343 nan 0.1000 -0.0015
880 0.6335 nan 0.1000 -0.0011
900 0.6318 nan 0.1000 -0.0006
920 0.6304 nan 0.1000 -0.0005
940 0.6296 nan 0.1000 -0.0014
960 0.6287 nan 0.1000 -0.0003
980 0.6275 nan 0.1000 -0.0009
1000 0.6261 nan 0.1000 -0.0004
1020 0.6248 nan 0.1000 -0.0011
1040 0.6233 nan 0.1000 -0.0004
1060 0.6223 nan 0.1000 -0.0008
1080 0.6209 nan 0.1000 -0.0002
1100 0.6200 nan 0.1000 -0.0006
- Fold07.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2543 nan 0.1000 0.0366
2 1.1905 nan 0.1000 0.0310
3 1.1441 nan 0.1000 0.0216
4 1.1024 nan 0.1000 0.0200
5 1.0670 nan 0.1000 0.0172
6 1.0335 nan 0.1000 0.0158
7 1.0063 nan 0.1000 0.0123
8 0.9828 nan 0.1000 0.0104
9 0.9637 nan 0.1000 0.0082
10 0.9461 nan 0.1000 0.0091
20 0.8480 nan 0.1000 0.0019
40 0.7695 nan 0.1000 -0.0000
60 0.7300 nan 0.1000 0.0001
80 0.7045 nan 0.1000 -0.0011
100 0.6827 nan 0.1000 -0.0001
120 0.6636 nan 0.1000 -0.0010
140 0.6517 nan 0.1000 -0.0007
160 0.6363 nan 0.1000 -0.0015
180 0.6289 nan 0.1000 -0.0004
200 0.6173 nan 0.1000 -0.0014
220 0.6038 nan 0.1000 -0.0008
240 0.5925 nan 0.1000 -0.0006
260 0.5832 nan 0.1000 -0.0010
280 0.5757 nan 0.1000 -0.0014
300 0.5652 nan 0.1000 -0.0004
320 0.5573 nan 0.1000 -0.0019
340 0.5496 nan 0.1000 -0.0010
360 0.5451 nan 0.1000 -0.0008
380 0.5360 nan 0.1000 -0.0009
400 0.5279 nan 0.1000 -0.0012
420 0.5210 nan 0.1000 -0.0021
440 0.5134 nan 0.1000 -0.0013
460 0.5048 nan 0.1000 -0.0002
480 0.4988 nan 0.1000 -0.0014
500 0.4924 nan 0.1000 -0.0008
520 0.4882 nan 0.1000 -0.0011
540 0.4828 nan 0.1000 -0.0003
560 0.4759 nan 0.1000 -0.0007
580 0.4700 nan 0.1000 -0.0009
600 0.4673 nan 0.1000 -0.0007
620 0.4619 nan 0.1000 -0.0011
640 0.4601 nan 0.1000 -0.0013
660 0.4538 nan 0.1000 -0.0007
680 0.4482 nan 0.1000 -0.0018
700 0.4431 nan 0.1000 -0.0017
720 0.4386 nan 0.1000 -0.0007
740 0.4353 nan 0.1000 -0.0008
760 0.4313 nan 0.1000 -0.0006
780 0.4283 nan 0.1000 -0.0009
800 0.4241 nan 0.1000 -0.0006
820 0.4199 nan 0.1000 -0.0015
840 0.4167 nan 0.1000 -0.0011
860 0.4137 nan 0.1000 -0.0009
880 0.4110 nan 0.1000 -0.0003
900 0.4089 nan 0.1000 -0.0009
920 0.4049 nan 0.1000 -0.0010
940 0.4008 nan 0.1000 -0.0019
960 0.3971 nan 0.1000 -0.0003
980 0.3946 nan 0.1000 -0.0006
1000 0.3916 nan 0.1000 -0.0015
1020 0.3887 nan 0.1000 -0.0014
1040 0.3872 nan 0.1000 -0.0007
1060 0.3836 nan 0.1000 -0.0007
1080 0.3776 nan 0.1000 -0.0004
1100 0.3746 nan 0.1000 -0.0005
- Fold07.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2508 nan 0.1000 0.0398
2 1.1812 nan 0.1000 0.0296
3 1.1247 nan 0.1000 0.0270
4 1.0767 nan 0.1000 0.0223
5 1.0328 nan 0.1000 0.0190
6 0.9984 nan 0.1000 0.0153
7 0.9705 nan 0.1000 0.0130
8 0.9461 nan 0.1000 0.0117
9 0.9265 nan 0.1000 0.0087
10 0.9092 nan 0.1000 0.0062
20 0.8045 nan 0.1000 0.0014
40 0.7274 nan 0.1000 0.0003
60 0.6859 nan 0.1000 -0.0008
80 0.6504 nan 0.1000 -0.0004
100 0.6250 nan 0.1000 -0.0009
120 0.6044 nan 0.1000 -0.0008
140 0.5838 nan 0.1000 -0.0009
160 0.5652 nan 0.1000 -0.0014
180 0.5503 nan 0.1000 -0.0003
200 0.5348 nan 0.1000 -0.0019
220 0.5191 nan 0.1000 -0.0011
240 0.5068 nan 0.1000 -0.0004
260 0.4921 nan 0.1000 -0.0004
280 0.4821 nan 0.1000 -0.0016
300 0.4728 nan 0.1000 -0.0014
320 0.4603 nan 0.1000 -0.0010
340 0.4529 nan 0.1000 -0.0006
360 0.4420 nan 0.1000 -0.0011
380 0.4319 nan 0.1000 -0.0013
400 0.4212 nan 0.1000 -0.0006
420 0.4124 nan 0.1000 -0.0018
440 0.4054 nan 0.1000 -0.0015
460 0.3965 nan 0.1000 -0.0011
480 0.3897 nan 0.1000 -0.0013
500 0.3809 nan 0.1000 -0.0007
520 0.3754 nan 0.1000 -0.0010
540 0.3690 nan 0.1000 -0.0009
560 0.3612 nan 0.1000 -0.0007
580 0.3548 nan 0.1000 -0.0011
600 0.3491 nan 0.1000 -0.0015
620 0.3432 nan 0.1000 -0.0005
640 0.3377 nan 0.1000 -0.0005
660 0.3320 nan 0.1000 -0.0009
680 0.3279 nan 0.1000 -0.0004
700 0.3208 nan 0.1000 -0.0007
720 0.3169 nan 0.1000 -0.0013
740 0.3140 nan 0.1000 -0.0012
760 0.3095 nan 0.1000 -0.0009
780 0.3044 nan 0.1000 -0.0017
800 0.3005 nan 0.1000 -0.0011
820 0.2961 nan 0.1000 -0.0011
840 0.2920 nan 0.1000 -0.0010
860 0.2889 nan 0.1000 -0.0011
880 0.2850 nan 0.1000 -0.0018
900 0.2799 nan 0.1000 -0.0011
920 0.2760 nan 0.1000 -0.0006
940 0.2730 nan 0.1000 -0.0010
960 0.2702 nan 0.1000 -0.0005
980 0.2673 nan 0.1000 -0.0009
1000 0.2637 nan 0.1000 -0.0009
1020 0.2612 nan 0.1000 -0.0010
1040 0.2579 nan 0.1000 -0.0005
1060 0.2551 nan 0.1000 -0.0005
1080 0.2526 nan 0.1000 -0.0009
1100 0.2489 nan 0.1000 -0.0006
- Fold07.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3259 nan 0.0100 0.0027
2 1.3205 nan 0.0100 0.0028
3 1.3146 nan 0.0100 0.0026
4 1.3089 nan 0.0100 0.0028
5 1.3036 nan 0.0100 0.0027
6 1.2984 nan 0.0100 0.0025
7 1.2927 nan 0.0100 0.0027
8 1.2871 nan 0.0100 0.0026
9 1.2820 nan 0.0100 0.0024
10 1.2766 nan 0.0100 0.0024
20 1.2339 nan 0.0100 0.0020
40 1.1671 nan 0.0100 0.0008
60 1.1167 nan 0.0100 0.0009
80 1.0814 nan 0.0100 0.0006
100 1.0528 nan 0.0100 0.0004
120 1.0276 nan 0.0100 0.0005
140 1.0069 nan 0.0100 0.0004
160 0.9899 nan 0.0100 0.0003
180 0.9756 nan 0.0100 0.0003
200 0.9633 nan 0.0100 0.0003
220 0.9516 nan 0.0100 0.0001
240 0.9416 nan 0.0100 0.0002
260 0.9329 nan 0.0100 0.0002
280 0.9252 nan 0.0100 -0.0001
300 0.9177 nan 0.0100 -0.0000
320 0.9111 nan 0.0100 0.0001
340 0.9055 nan 0.0100 0.0001
360 0.8998 nan 0.0100 0.0001
380 0.8948 nan 0.0100 -0.0001
400 0.8900 nan 0.0100 0.0001
420 0.8854 nan 0.0100 0.0000
440 0.8808 nan 0.0100 0.0000
460 0.8765 nan 0.0100 0.0001
480 0.8728 nan 0.0100 0.0001
500 0.8690 nan 0.0100 -0.0000
520 0.8653 nan 0.0100 0.0000
540 0.8614 nan 0.0100 -0.0000
560 0.8578 nan 0.0100 -0.0001
580 0.8548 nan 0.0100 -0.0000
600 0.8516 nan 0.0100 0.0000
620 0.8487 nan 0.0100 0.0000
640 0.8457 nan 0.0100 -0.0000
660 0.8428 nan 0.0100 -0.0000
680 0.8404 nan 0.0100 -0.0000
700 0.8380 nan 0.0100 -0.0000
720 0.8355 nan 0.0100 -0.0002
740 0.8333 nan 0.0100 0.0000
760 0.8311 nan 0.0100 0.0000
780 0.8289 nan 0.0100 0.0000
800 0.8268 nan 0.0100 -0.0002
820 0.8247 nan 0.0100 -0.0000
840 0.8228 nan 0.0100 -0.0001
860 0.8209 nan 0.0100 0.0000
880 0.8190 nan 0.0100 -0.0001
900 0.8170 nan 0.0100 0.0000
920 0.8154 nan 0.0100 -0.0001
940 0.8138 nan 0.0100 -0.0000
960 0.8122 nan 0.0100 -0.0001
980 0.8106 nan 0.0100 -0.0000
1000 0.8092 nan 0.0100 -0.0000
1020 0.8075 nan 0.0100 -0.0000
1040 0.8062 nan 0.0100 -0.0000
1060 0.8048 nan 0.0100 -0.0000
1080 0.8032 nan 0.0100 -0.0000
1100 0.8020 nan 0.0100 -0.0001
- Fold08.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3249 nan 0.0100 0.0034
2 1.3181 nan 0.0100 0.0033
3 1.3111 nan 0.0100 0.0030
4 1.3047 nan 0.0100 0.0033
5 1.2984 nan 0.0100 0.0033
6 1.2917 nan 0.0100 0.0032
7 1.2857 nan 0.0100 0.0030
8 1.2795 nan 0.0100 0.0029
9 1.2733 nan 0.0100 0.0030
10 1.2672 nan 0.0100 0.0029
20 1.2113 nan 0.0100 0.0024
40 1.1277 nan 0.0100 0.0018
60 1.0651 nan 0.0100 0.0013
80 1.0186 nan 0.0100 0.0010
100 0.9819 nan 0.0100 0.0003
120 0.9553 nan 0.0100 0.0003
140 0.9332 nan 0.0100 0.0003
160 0.9150 nan 0.0100 0.0003
180 0.9004 nan 0.0100 0.0003
200 0.8860 nan 0.0100 0.0003
220 0.8748 nan 0.0100 0.0001
240 0.8646 nan 0.0100 0.0000
260 0.8550 nan 0.0100 0.0001
280 0.8468 nan 0.0100 0.0001
300 0.8396 nan 0.0100 0.0001
320 0.8327 nan 0.0100 0.0001
340 0.8267 nan 0.0100 0.0001
360 0.8202 nan 0.0100 -0.0001
380 0.8146 nan 0.0100 0.0001
400 0.8097 nan 0.0100 0.0000
420 0.8044 nan 0.0100 -0.0000
440 0.7996 nan 0.0100 -0.0001
460 0.7956 nan 0.0100 -0.0001
480 0.7914 nan 0.0100 -0.0001
500 0.7874 nan 0.0100 -0.0000
520 0.7835 nan 0.0100 0.0000
540 0.7800 nan 0.0100 -0.0001
560 0.7765 nan 0.0100 -0.0000
580 0.7732 nan 0.0100 -0.0001
600 0.7697 nan 0.0100 -0.0000
620 0.7664 nan 0.0100 -0.0001
640 0.7632 nan 0.0100 -0.0000
660 0.7603 nan 0.0100 -0.0000
680 0.7572 nan 0.0100 -0.0001
700 0.7542 nan 0.0100 -0.0000
720 0.7510 nan 0.0100 -0.0001
740 0.7483 nan 0.0100 -0.0001
760 0.7463 nan 0.0100 -0.0001
780 0.7442 nan 0.0100 -0.0001
800 0.7421 nan 0.0100 -0.0001
820 0.7397 nan 0.0100 -0.0001
840 0.7370 nan 0.0100 0.0000
860 0.7345 nan 0.0100 -0.0001
880 0.7323 nan 0.0100 -0.0001
900 0.7301 nan 0.0100 -0.0001
920 0.7282 nan 0.0100 -0.0001
940 0.7261 nan 0.0100 -0.0001
960 0.7237 nan 0.0100 -0.0001
980 0.7220 nan 0.0100 -0.0001
1000 0.7199 nan 0.0100 -0.0001
1020 0.7179 nan 0.0100 -0.0000
1040 0.7163 nan 0.0100 -0.0001
1060 0.7144 nan 0.0100 -0.0001
1080 0.7123 nan 0.0100 -0.0000
1100 0.7109 nan 0.0100 -0.0001
- Fold08.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0038
2 1.3167 nan 0.0100 0.0036
3 1.3089 nan 0.0100 0.0039
4 1.3019 nan 0.0100 0.0036
5 1.2953 nan 0.0100 0.0032
6 1.2881 nan 0.0100 0.0033
7 1.2809 nan 0.0100 0.0032
8 1.2739 nan 0.0100 0.0035
9 1.2671 nan 0.0100 0.0032
10 1.2603 nan 0.0100 0.0032
20 1.1998 nan 0.0100 0.0024
40 1.1048 nan 0.0100 0.0021
60 1.0378 nan 0.0100 0.0014
80 0.9865 nan 0.0100 0.0009
100 0.9489 nan 0.0100 0.0005
120 0.9196 nan 0.0100 0.0005
140 0.8956 nan 0.0100 0.0005
160 0.8773 nan 0.0100 0.0002
180 0.8614 nan 0.0100 0.0003
200 0.8482 nan 0.0100 0.0001
220 0.8355 nan 0.0100 0.0001
240 0.8246 nan 0.0100 -0.0000
260 0.8151 nan 0.0100 0.0001
280 0.8067 nan 0.0100 0.0000
300 0.7983 nan 0.0100 0.0000
320 0.7907 nan 0.0100 -0.0000
340 0.7842 nan 0.0100 -0.0001
360 0.7780 nan 0.0100 -0.0001
380 0.7722 nan 0.0100 -0.0001
400 0.7665 nan 0.0100 -0.0001
420 0.7611 nan 0.0100 0.0000
440 0.7558 nan 0.0100 -0.0000
460 0.7507 nan 0.0100 -0.0000
480 0.7459 nan 0.0100 -0.0000
500 0.7408 nan 0.0100 -0.0000
520 0.7363 nan 0.0100 -0.0000
540 0.7321 nan 0.0100 -0.0002
560 0.7278 nan 0.0100 -0.0001
580 0.7242 nan 0.0100 -0.0001
600 0.7205 nan 0.0100 -0.0001
620 0.7167 nan 0.0100 0.0000
640 0.7132 nan 0.0100 -0.0001
660 0.7094 nan 0.0100 -0.0001
680 0.7054 nan 0.0100 -0.0001
700 0.7022 nan 0.0100 -0.0002
720 0.6990 nan 0.0100 -0.0001
740 0.6957 nan 0.0100 -0.0001
760 0.6924 nan 0.0100 -0.0002
780 0.6893 nan 0.0100 -0.0001
800 0.6861 nan 0.0100 -0.0001
820 0.6834 nan 0.0100 -0.0001
840 0.6803 nan 0.0100 -0.0002
860 0.6773 nan 0.0100 -0.0000
880 0.6741 nan 0.0100 -0.0001
900 0.6710 nan 0.0100 -0.0000
920 0.6684 nan 0.0100 -0.0000
940 0.6659 nan 0.0100 -0.0001
960 0.6630 nan 0.0100 -0.0000
980 0.6604 nan 0.0100 -0.0000
1000 0.6579 nan 0.0100 -0.0001
1020 0.6556 nan 0.0100 -0.0001
1040 0.6530 nan 0.0100 -0.0001
1060 0.6512 nan 0.0100 -0.0002
1080 0.6483 nan 0.0100 0.0001
1100 0.6453 nan 0.0100 -0.0002
- Fold08.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2749 nan 0.1000 0.0278
2 1.2317 nan 0.1000 0.0227
3 1.2004 nan 0.1000 0.0182
4 1.1667 nan 0.1000 0.0162
5 1.1385 nan 0.1000 0.0125
6 1.1161 nan 0.1000 0.0108
7 1.0981 nan 0.1000 0.0091
8 1.0840 nan 0.1000 0.0078
9 1.0684 nan 0.1000 0.0069
10 1.0544 nan 0.1000 0.0060
20 0.9618 nan 0.1000 0.0010
40 0.8876 nan 0.1000 -0.0000
60 0.8496 nan 0.1000 -0.0002
80 0.8272 nan 0.1000 0.0001
100 0.8095 nan 0.1000 -0.0007
120 0.7981 nan 0.1000 -0.0004
140 0.7870 nan 0.1000 -0.0015
160 0.7797 nan 0.1000 -0.0006
180 0.7716 nan 0.1000 -0.0015
200 0.7646 nan 0.1000 -0.0001
220 0.7570 nan 0.1000 -0.0008
240 0.7521 nan 0.1000 -0.0013
260 0.7472 nan 0.1000 0.0001
280 0.7440 nan 0.1000 -0.0006
300 0.7392 nan 0.1000 -0.0010
320 0.7351 nan 0.1000 -0.0008
340 0.7316 nan 0.1000 -0.0007
360 0.7271 nan 0.1000 -0.0007
380 0.7240 nan 0.1000 -0.0006
400 0.7207 nan 0.1000 -0.0007
420 0.7179 nan 0.1000 -0.0004
440 0.7145 nan 0.1000 -0.0004
460 0.7125 nan 0.1000 -0.0004
480 0.7085 nan 0.1000 -0.0007
500 0.7076 nan 0.1000 -0.0007
520 0.7051 nan 0.1000 -0.0007
540 0.7026 nan 0.1000 -0.0009
560 0.6997 nan 0.1000 -0.0007
580 0.6964 nan 0.1000 -0.0003
600 0.6947 nan 0.1000 -0.0010
620 0.6918 nan 0.1000 -0.0006
640 0.6899 nan 0.1000 -0.0005
660 0.6880 nan 0.1000 -0.0008
680 0.6863 nan 0.1000 -0.0008
700 0.6834 nan 0.1000 -0.0010
720 0.6817 nan 0.1000 -0.0008
740 0.6811 nan 0.1000 -0.0005
760 0.6795 nan 0.1000 -0.0008
780 0.6775 nan 0.1000 -0.0009
800 0.6753 nan 0.1000 -0.0007
820 0.6739 nan 0.1000 -0.0010
840 0.6722 nan 0.1000 -0.0006
860 0.6693 nan 0.1000 -0.0008
880 0.6673 nan 0.1000 -0.0004
900 0.6650 nan 0.1000 -0.0008
920 0.6635 nan 0.1000 -0.0010
940 0.6618 nan 0.1000 -0.0008
960 0.6597 nan 0.1000 -0.0004
980 0.6583 nan 0.1000 -0.0004
1000 0.6570 nan 0.1000 -0.0007
1020 0.6564 nan 0.1000 -0.0011
1040 0.6536 nan 0.1000 -0.0004
1060 0.6534 nan 0.1000 -0.0009
1080 0.6519 nan 0.1000 -0.0014
1100 0.6514 nan 0.1000 -0.0006
- Fold08.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2651 nan 0.1000 0.0335
2 1.2074 nan 0.1000 0.0287
3 1.1628 nan 0.1000 0.0217
4 1.1244 nan 0.1000 0.0192
5 1.0921 nan 0.1000 0.0162
6 1.0608 nan 0.1000 0.0141
7 1.0330 nan 0.1000 0.0117
8 1.0131 nan 0.1000 0.0093
9 0.9941 nan 0.1000 0.0078
10 0.9782 nan 0.1000 0.0057
20 0.8915 nan 0.1000 -0.0004
40 0.8075 nan 0.1000 0.0006
60 0.7692 nan 0.1000 0.0004
80 0.7406 nan 0.1000 -0.0005
100 0.7223 nan 0.1000 -0.0003
120 0.7054 nan 0.1000 -0.0009
140 0.6906 nan 0.1000 0.0004
160 0.6754 nan 0.1000 -0.0011
180 0.6618 nan 0.1000 -0.0011
200 0.6497 nan 0.1000 -0.0013
220 0.6350 nan 0.1000 -0.0003
240 0.6229 nan 0.1000 -0.0005
260 0.6134 nan 0.1000 -0.0014
280 0.6024 nan 0.1000 -0.0008
300 0.5888 nan 0.1000 -0.0014
320 0.5792 nan 0.1000 -0.0009
340 0.5726 nan 0.1000 -0.0006
360 0.5660 nan 0.1000 -0.0004
380 0.5582 nan 0.1000 -0.0013
400 0.5515 nan 0.1000 -0.0004
420 0.5428 nan 0.1000 -0.0001
440 0.5367 nan 0.1000 -0.0012
460 0.5290 nan 0.1000 -0.0008
480 0.5246 nan 0.1000 -0.0022
500 0.5204 nan 0.1000 -0.0011
520 0.5151 nan 0.1000 -0.0012
540 0.5075 nan 0.1000 -0.0006
560 0.5021 nan 0.1000 -0.0008
580 0.4954 nan 0.1000 -0.0011
600 0.4888 nan 0.1000 -0.0015
620 0.4849 nan 0.1000 -0.0009
640 0.4792 nan 0.1000 -0.0011
660 0.4724 nan 0.1000 -0.0009
680 0.4662 nan 0.1000 -0.0005
700 0.4607 nan 0.1000 -0.0008
720 0.4563 nan 0.1000 -0.0009
740 0.4514 nan 0.1000 -0.0012
760 0.4474 nan 0.1000 -0.0014
780 0.4446 nan 0.1000 -0.0010
800 0.4425 nan 0.1000 -0.0008
820 0.4368 nan 0.1000 -0.0006
840 0.4315 nan 0.1000 -0.0003
860 0.4280 nan 0.1000 -0.0006
880 0.4243 nan 0.1000 -0.0006
900 0.4183 nan 0.1000 -0.0005
920 0.4133 nan 0.1000 -0.0006
940 0.4102 nan 0.1000 -0.0007
960 0.4071 nan 0.1000 -0.0006
980 0.4025 nan 0.1000 -0.0010
1000 0.3993 nan 0.1000 -0.0007
1020 0.3973 nan 0.1000 -0.0010
1040 0.3940 nan 0.1000 -0.0009
1060 0.3914 nan 0.1000 -0.0009
1080 0.3877 nan 0.1000 -0.0007
1100 0.3839 nan 0.1000 -0.0004
- Fold08.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2531 nan 0.1000 0.0359
2 1.1865 nan 0.1000 0.0288
3 1.1348 nan 0.1000 0.0244
4 1.0943 nan 0.1000 0.0205
5 1.0586 nan 0.1000 0.0167
6 1.0272 nan 0.1000 0.0144
7 0.9995 nan 0.1000 0.0130
8 0.9772 nan 0.1000 0.0101
9 0.9562 nan 0.1000 0.0068
10 0.9406 nan 0.1000 0.0068
20 0.8486 nan 0.1000 0.0019
40 0.7721 nan 0.1000 0.0001
60 0.7258 nan 0.1000 -0.0015
80 0.6927 nan 0.1000 -0.0019
100 0.6623 nan 0.1000 -0.0017
120 0.6394 nan 0.1000 -0.0010
140 0.6181 nan 0.1000 -0.0007
160 0.5963 nan 0.1000 -0.0026
180 0.5794 nan 0.1000 -0.0012
200 0.5602 nan 0.1000 -0.0008
220 0.5453 nan 0.1000 -0.0010
240 0.5287 nan 0.1000 -0.0015
260 0.5161 nan 0.1000 -0.0015
280 0.5044 nan 0.1000 -0.0010
300 0.4929 nan 0.1000 -0.0014
320 0.4822 nan 0.1000 -0.0005
340 0.4716 nan 0.1000 -0.0017
360 0.4565 nan 0.1000 -0.0017
380 0.4490 nan 0.1000 -0.0025
400 0.4390 nan 0.1000 -0.0008
420 0.4274 nan 0.1000 -0.0016
440 0.4195 nan 0.1000 -0.0015
460 0.4109 nan 0.1000 -0.0009
480 0.4023 nan 0.1000 -0.0013
500 0.3952 nan 0.1000 -0.0012
520 0.3855 nan 0.1000 -0.0006
540 0.3783 nan 0.1000 -0.0006
560 0.3718 nan 0.1000 -0.0005
580 0.3664 nan 0.1000 -0.0010
600 0.3609 nan 0.1000 -0.0006
620 0.3564 nan 0.1000 -0.0010
640 0.3515 nan 0.1000 -0.0009
660 0.3458 nan 0.1000 -0.0004
680 0.3400 nan 0.1000 -0.0007
700 0.3338 nan 0.1000 -0.0011
720 0.3296 nan 0.1000 -0.0005
740 0.3244 nan 0.1000 -0.0006
760 0.3204 nan 0.1000 -0.0019
780 0.3154 nan 0.1000 -0.0004
800 0.3109 nan 0.1000 -0.0008
820 0.3071 nan 0.1000 -0.0014
840 0.3022 nan 0.1000 -0.0009
860 0.2979 nan 0.1000 -0.0013
880 0.2946 nan 0.1000 -0.0008
900 0.2896 nan 0.1000 -0.0009
920 0.2851 nan 0.1000 -0.0013
940 0.2802 nan 0.1000 -0.0011
960 0.2764 nan 0.1000 -0.0010
980 0.2726 nan 0.1000 -0.0006
1000 0.2704 nan 0.1000 -0.0006
1020 0.2663 nan 0.1000 -0.0015
1040 0.2635 nan 0.1000 -0.0010
1060 0.2591 nan 0.1000 -0.0011
1080 0.2556 nan 0.1000 -0.0005
1100 0.2532 nan 0.1000 -0.0007
- Fold08.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3251 nan 0.0100 0.0030
2 1.3187 nan 0.0100 0.0030
3 1.3128 nan 0.0100 0.0030
4 1.3068 nan 0.0100 0.0028
5 1.3014 nan 0.0100 0.0027
6 1.2956 nan 0.0100 0.0027
7 1.2897 nan 0.0100 0.0026
8 1.2844 nan 0.0100 0.0027
9 1.2790 nan 0.0100 0.0025
10 1.2741 nan 0.0100 0.0025
20 1.2272 nan 0.0100 0.0020
40 1.1558 nan 0.0100 0.0015
60 1.1076 nan 0.0100 0.0011
80 1.0704 nan 0.0100 0.0007
100 1.0406 nan 0.0100 0.0006
120 1.0158 nan 0.0100 0.0005
140 0.9952 nan 0.0100 0.0003
160 0.9776 nan 0.0100 0.0003
180 0.9630 nan 0.0100 0.0003
200 0.9498 nan 0.0100 0.0003
220 0.9386 nan 0.0100 0.0001
240 0.9278 nan 0.0100 0.0002
260 0.9183 nan 0.0100 -0.0000
280 0.9099 nan 0.0100 0.0001
300 0.9020 nan 0.0100 0.0001
320 0.8950 nan 0.0100 0.0002
340 0.8889 nan 0.0100 -0.0001
360 0.8831 nan 0.0100 0.0001
380 0.8774 nan 0.0100 0.0000
400 0.8718 nan 0.0100 0.0000
420 0.8668 nan 0.0100 0.0000
440 0.8619 nan 0.0100 0.0000
460 0.8575 nan 0.0100 0.0000
480 0.8534 nan 0.0100 -0.0000
500 0.8496 nan 0.0100 -0.0001
520 0.8461 nan 0.0100 -0.0001
540 0.8429 nan 0.0100 0.0000
560 0.8396 nan 0.0100 0.0000
580 0.8365 nan 0.0100 0.0000
600 0.8335 nan 0.0100 0.0000
620 0.8306 nan 0.0100 0.0000
640 0.8279 nan 0.0100 0.0000
660 0.8253 nan 0.0100 -0.0000
680 0.8228 nan 0.0100 -0.0000
700 0.8204 nan 0.0100 -0.0000
720 0.8180 nan 0.0100 0.0000
740 0.8157 nan 0.0100 0.0000
760 0.8137 nan 0.0100 -0.0000
780 0.8114 nan 0.0100 0.0000
800 0.8097 nan 0.0100 -0.0000
820 0.8077 nan 0.0100 -0.0001
840 0.8059 nan 0.0100 -0.0000
860 0.8041 nan 0.0100 -0.0000
880 0.8021 nan 0.0100 -0.0001
900 0.8004 nan 0.0100 0.0000
920 0.7988 nan 0.0100 -0.0001
940 0.7974 nan 0.0100 -0.0000
960 0.7958 nan 0.0100 -0.0000
980 0.7946 nan 0.0100 -0.0001
1000 0.7932 nan 0.0100 -0.0000
1020 0.7917 nan 0.0100 -0.0000
1040 0.7903 nan 0.0100 -0.0000
1060 0.7888 nan 0.0100 -0.0000
1080 0.7873 nan 0.0100 0.0000
1100 0.7862 nan 0.0100 -0.0001
- Fold09.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0037
2 1.3165 nan 0.0100 0.0037
3 1.3092 nan 0.0100 0.0035
4 1.3019 nan 0.0100 0.0033
5 1.2943 nan 0.0100 0.0035
6 1.2875 nan 0.0100 0.0034
7 1.2806 nan 0.0100 0.0034
8 1.2739 nan 0.0100 0.0031
9 1.2674 nan 0.0100 0.0031
10 1.2608 nan 0.0100 0.0031
20 1.2022 nan 0.0100 0.0026
40 1.1118 nan 0.0100 0.0017
60 1.0468 nan 0.0100 0.0014
80 0.9989 nan 0.0100 0.0009
100 0.9638 nan 0.0100 0.0005
120 0.9343 nan 0.0100 0.0005
140 0.9103 nan 0.0100 0.0004
160 0.8913 nan 0.0100 0.0002
180 0.8758 nan 0.0100 0.0003
200 0.8629 nan 0.0100 0.0001
220 0.8513 nan 0.0100 -0.0000
240 0.8415 nan 0.0100 0.0000
260 0.8319 nan 0.0100 0.0001
280 0.8240 nan 0.0100 -0.0000
300 0.8170 nan 0.0100 -0.0001
320 0.8103 nan 0.0100 0.0001
340 0.8038 nan 0.0100 -0.0000
360 0.7982 nan 0.0100 0.0001
380 0.7925 nan 0.0100 0.0000
400 0.7876 nan 0.0100 -0.0000
420 0.7837 nan 0.0100 -0.0001
440 0.7793 nan 0.0100 0.0000
460 0.7751 nan 0.0100 -0.0001
480 0.7716 nan 0.0100 -0.0001
500 0.7679 nan 0.0100 -0.0001
520 0.7648 nan 0.0100 -0.0001
540 0.7614 nan 0.0100 -0.0000
560 0.7584 nan 0.0100 -0.0001
580 0.7552 nan 0.0100 -0.0001
600 0.7527 nan 0.0100 -0.0001
620 0.7497 nan 0.0100 -0.0000
640 0.7470 nan 0.0100 -0.0001
660 0.7443 nan 0.0100 -0.0001
680 0.7417 nan 0.0100 -0.0000
700 0.7395 nan 0.0100 -0.0002
720 0.7372 nan 0.0100 -0.0001
740 0.7350 nan 0.0100 -0.0000
760 0.7330 nan 0.0100 -0.0001
780 0.7309 nan 0.0100 -0.0001
800 0.7289 nan 0.0100 -0.0001
820 0.7266 nan 0.0100 -0.0001
840 0.7249 nan 0.0100 -0.0001
860 0.7222 nan 0.0100 -0.0001
880 0.7206 nan 0.0100 -0.0001
900 0.7185 nan 0.0100 -0.0000
920 0.7163 nan 0.0100 -0.0001
940 0.7144 nan 0.0100 -0.0001
960 0.7125 nan 0.0100 -0.0002
980 0.7108 nan 0.0100 -0.0000
1000 0.7089 nan 0.0100 -0.0001
1020 0.7071 nan 0.0100 -0.0001
1040 0.7051 nan 0.0100 -0.0001
1060 0.7033 nan 0.0100 -0.0000
1080 0.7017 nan 0.0100 -0.0001
1100 0.6998 nan 0.0100 0.0000
- Fold09.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3238 nan 0.0100 0.0039
2 1.3159 nan 0.0100 0.0038
3 1.3081 nan 0.0100 0.0037
4 1.3002 nan 0.0100 0.0038
5 1.2922 nan 0.0100 0.0039
6 1.2847 nan 0.0100 0.0039
7 1.2773 nan 0.0100 0.0038
8 1.2698 nan 0.0100 0.0036
9 1.2628 nan 0.0100 0.0034
10 1.2558 nan 0.0100 0.0036
20 1.1932 nan 0.0100 0.0028
40 1.0933 nan 0.0100 0.0018
60 1.0209 nan 0.0100 0.0013
80 0.9677 nan 0.0100 0.0011
100 0.9281 nan 0.0100 0.0007
120 0.8975 nan 0.0100 0.0003
140 0.8729 nan 0.0100 0.0005
160 0.8534 nan 0.0100 0.0001
180 0.8375 nan 0.0100 0.0000
200 0.8239 nan 0.0100 0.0002
220 0.8124 nan 0.0100 0.0002
240 0.8015 nan 0.0100 0.0001
260 0.7922 nan 0.0100 0.0000
280 0.7840 nan 0.0100 0.0000
300 0.7764 nan 0.0100 0.0000
320 0.7697 nan 0.0100 0.0000
340 0.7630 nan 0.0100 -0.0000
360 0.7573 nan 0.0100 0.0001
380 0.7516 nan 0.0100 -0.0000
400 0.7468 nan 0.0100 -0.0001
420 0.7424 nan 0.0100 -0.0001
440 0.7375 nan 0.0100 -0.0001
460 0.7334 nan 0.0100 -0.0001
480 0.7292 nan 0.0100 -0.0001
500 0.7251 nan 0.0100 -0.0000
520 0.7217 nan 0.0100 -0.0001
540 0.7180 nan 0.0100 -0.0001
560 0.7148 nan 0.0100 -0.0001
580 0.7111 nan 0.0100 -0.0002
600 0.7077 nan 0.0100 -0.0001
620 0.7044 nan 0.0100 -0.0001
640 0.7006 nan 0.0100 -0.0000
660 0.6978 nan 0.0100 -0.0001
680 0.6943 nan 0.0100 -0.0001
700 0.6908 nan 0.0100 -0.0000
720 0.6877 nan 0.0100 -0.0002
740 0.6854 nan 0.0100 -0.0002
760 0.6827 nan 0.0100 -0.0000
780 0.6795 nan 0.0100 -0.0001
800 0.6765 nan 0.0100 -0.0001
820 0.6733 nan 0.0100 -0.0001
840 0.6700 nan 0.0100 -0.0002
860 0.6672 nan 0.0100 -0.0001
880 0.6642 nan 0.0100 -0.0001
900 0.6616 nan 0.0100 -0.0002
920 0.6589 nan 0.0100 -0.0001
940 0.6565 nan 0.0100 -0.0001
960 0.6538 nan 0.0100 -0.0001
980 0.6511 nan 0.0100 -0.0001
1000 0.6486 nan 0.0100 -0.0000
1020 0.6464 nan 0.0100 -0.0001
1040 0.6442 nan 0.0100 -0.0001
1060 0.6416 nan 0.0100 -0.0001
1080 0.6393 nan 0.0100 -0.0001
1100 0.6369 nan 0.0100 -0.0001
- Fold09.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2676 nan 0.1000 0.0274
2 1.2192 nan 0.1000 0.0238
3 1.1824 nan 0.1000 0.0180
4 1.1511 nan 0.1000 0.0162
5 1.1208 nan 0.1000 0.0131
6 1.1013 nan 0.1000 0.0108
7 1.0820 nan 0.1000 0.0090
8 1.0655 nan 0.1000 0.0067
9 1.0478 nan 0.1000 0.0076
10 1.0349 nan 0.1000 0.0060
20 0.9531 nan 0.1000 0.0039
40 0.8725 nan 0.1000 0.0013
60 0.8341 nan 0.1000 -0.0001
80 0.8083 nan 0.1000 -0.0007
100 0.7910 nan 0.1000 -0.0006
120 0.7787 nan 0.1000 -0.0006
140 0.7690 nan 0.1000 -0.0004
160 0.7619 nan 0.1000 -0.0003
180 0.7557 nan 0.1000 -0.0003
200 0.7497 nan 0.1000 -0.0001
220 0.7442 nan 0.1000 -0.0015
240 0.7381 nan 0.1000 -0.0009
260 0.7336 nan 0.1000 -0.0002
280 0.7290 nan 0.1000 -0.0008
300 0.7241 nan 0.1000 -0.0004
320 0.7219 nan 0.1000 -0.0006
340 0.7193 nan 0.1000 -0.0006
360 0.7162 nan 0.1000 -0.0010
380 0.7122 nan 0.1000 -0.0007
400 0.7092 nan 0.1000 -0.0010
420 0.7060 nan 0.1000 -0.0003
440 0.7033 nan 0.1000 -0.0007
460 0.6997 nan 0.1000 -0.0009
480 0.6971 nan 0.1000 -0.0005
500 0.6943 nan 0.1000 -0.0014
520 0.6921 nan 0.1000 -0.0012
540 0.6895 nan 0.1000 -0.0003
560 0.6884 nan 0.1000 -0.0006
580 0.6851 nan 0.1000 -0.0003
600 0.6839 nan 0.1000 -0.0017
620 0.6823 nan 0.1000 -0.0007
640 0.6792 nan 0.1000 -0.0009
660 0.6776 nan 0.1000 -0.0016
680 0.6762 nan 0.1000 -0.0004
700 0.6733 nan 0.1000 -0.0012
720 0.6716 nan 0.1000 -0.0009
740 0.6704 nan 0.1000 -0.0002
760 0.6683 nan 0.1000 -0.0013
780 0.6662 nan 0.1000 -0.0012
800 0.6635 nan 0.1000 -0.0007
820 0.6615 nan 0.1000 -0.0012
840 0.6611 nan 0.1000 -0.0010
860 0.6593 nan 0.1000 -0.0011
880 0.6573 nan 0.1000 -0.0005
900 0.6556 nan 0.1000 -0.0013
920 0.6537 nan 0.1000 -0.0007
940 0.6523 nan 0.1000 -0.0009
960 0.6511 nan 0.1000 -0.0006
980 0.6503 nan 0.1000 -0.0005
1000 0.6490 nan 0.1000 -0.0004
1020 0.6464 nan 0.1000 -0.0008
1040 0.6463 nan 0.1000 -0.0018
1060 0.6448 nan 0.1000 -0.0016
1080 0.6423 nan 0.1000 -0.0006
1100 0.6401 nan 0.1000 -0.0002
- Fold09.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2551 nan 0.1000 0.0347
2 1.2004 nan 0.1000 0.0289
3 1.1511 nan 0.1000 0.0236
4 1.1083 nan 0.1000 0.0214
5 1.0709 nan 0.1000 0.0169
6 1.0404 nan 0.1000 0.0140
7 1.0149 nan 0.1000 0.0122
8 0.9944 nan 0.1000 0.0114
9 0.9758 nan 0.1000 0.0083
10 0.9597 nan 0.1000 0.0061
20 0.8651 nan 0.1000 0.0018
40 0.7845 nan 0.1000 0.0006
60 0.7492 nan 0.1000 -0.0014
80 0.7263 nan 0.1000 -0.0014
100 0.7047 nan 0.1000 -0.0009
120 0.6861 nan 0.1000 -0.0015
140 0.6705 nan 0.1000 -0.0010
160 0.6583 nan 0.1000 -0.0010
180 0.6410 nan 0.1000 -0.0002
200 0.6307 nan 0.1000 -0.0010
220 0.6199 nan 0.1000 -0.0007
240 0.6094 nan 0.1000 -0.0009
260 0.6008 nan 0.1000 -0.0012
280 0.5916 nan 0.1000 -0.0009
300 0.5824 nan 0.1000 -0.0011
320 0.5759 nan 0.1000 -0.0015
340 0.5688 nan 0.1000 -0.0011
360 0.5622 nan 0.1000 -0.0016
380 0.5543 nan 0.1000 -0.0017
400 0.5466 nan 0.1000 -0.0008
420 0.5392 nan 0.1000 -0.0009
440 0.5347 nan 0.1000 -0.0018
460 0.5274 nan 0.1000 -0.0011
480 0.5183 nan 0.1000 -0.0008
500 0.5152 nan 0.1000 -0.0004
520 0.5090 nan 0.1000 -0.0004
540 0.5030 nan 0.1000 -0.0013
560 0.4981 nan 0.1000 -0.0014
580 0.4909 nan 0.1000 -0.0007
600 0.4862 nan 0.1000 -0.0010
620 0.4820 nan 0.1000 -0.0009
640 0.4782 nan 0.1000 -0.0008
660 0.4742 nan 0.1000 -0.0006
680 0.4701 nan 0.1000 -0.0008
700 0.4650 nan 0.1000 -0.0009
720 0.4605 nan 0.1000 -0.0006
740 0.4551 nan 0.1000 -0.0013
760 0.4486 nan 0.1000 -0.0015
780 0.4429 nan 0.1000 -0.0012
800 0.4390 nan 0.1000 -0.0011
820 0.4340 nan 0.1000 -0.0010
840 0.4312 nan 0.1000 -0.0020
860 0.4283 nan 0.1000 -0.0003
880 0.4248 nan 0.1000 -0.0008
900 0.4217 nan 0.1000 -0.0009
920 0.4164 nan 0.1000 -0.0004
940 0.4119 nan 0.1000 -0.0007
960 0.4085 nan 0.1000 -0.0011
980 0.4066 nan 0.1000 -0.0006
1000 0.4026 nan 0.1000 -0.0006
1020 0.4004 nan 0.1000 -0.0010
1040 0.3975 nan 0.1000 -0.0011
1060 0.3943 nan 0.1000 -0.0003
1080 0.3920 nan 0.1000 -0.0008
1100 0.3896 nan 0.1000 -0.0007
- Fold09.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2576 nan 0.1000 0.0367
2 1.1939 nan 0.1000 0.0325
3 1.1386 nan 0.1000 0.0255
4 1.0925 nan 0.1000 0.0231
5 1.0539 nan 0.1000 0.0191
6 1.0224 nan 0.1000 0.0163
7 0.9950 nan 0.1000 0.0129
8 0.9703 nan 0.1000 0.0104
9 0.9477 nan 0.1000 0.0095
10 0.9306 nan 0.1000 0.0075
20 0.8244 nan 0.1000 0.0030
40 0.7470 nan 0.1000 -0.0002
60 0.7070 nan 0.1000 -0.0007
80 0.6790 nan 0.1000 -0.0009
100 0.6525 nan 0.1000 -0.0013
120 0.6286 nan 0.1000 -0.0019
140 0.6115 nan 0.1000 -0.0022
160 0.5935 nan 0.1000 -0.0004
180 0.5760 nan 0.1000 -0.0025
200 0.5611 nan 0.1000 -0.0006
220 0.5444 nan 0.1000 -0.0014
240 0.5333 nan 0.1000 -0.0009
260 0.5209 nan 0.1000 -0.0006
280 0.5085 nan 0.1000 -0.0011
300 0.4977 nan 0.1000 -0.0009
320 0.4858 nan 0.1000 -0.0011
340 0.4773 nan 0.1000 -0.0016
360 0.4677 nan 0.1000 -0.0019
380 0.4570 nan 0.1000 -0.0004
400 0.4513 nan 0.1000 -0.0014
420 0.4443 nan 0.1000 -0.0007
440 0.4338 nan 0.1000 -0.0010
460 0.4277 nan 0.1000 -0.0007
480 0.4208 nan 0.1000 -0.0006
500 0.4132 nan 0.1000 -0.0008
520 0.4049 nan 0.1000 -0.0008
540 0.3991 nan 0.1000 -0.0010
560 0.3913 nan 0.1000 -0.0011
580 0.3886 nan 0.1000 -0.0011
600 0.3841 nan 0.1000 -0.0009
620 0.3801 nan 0.1000 -0.0016
640 0.3726 nan 0.1000 -0.0007
660 0.3663 nan 0.1000 -0.0012
680 0.3615 nan 0.1000 -0.0007
700 0.3555 nan 0.1000 -0.0014
720 0.3510 nan 0.1000 -0.0011
740 0.3464 nan 0.1000 -0.0007
760 0.3420 nan 0.1000 -0.0018
780 0.3361 nan 0.1000 -0.0011
800 0.3307 nan 0.1000 -0.0006
820 0.3266 nan 0.1000 -0.0010
840 0.3224 nan 0.1000 -0.0010
860 0.3177 nan 0.1000 -0.0010
880 0.3128 nan 0.1000 -0.0011
900 0.3074 nan 0.1000 -0.0014
920 0.3029 nan 0.1000 -0.0008
940 0.2987 nan 0.1000 -0.0015
960 0.2941 nan 0.1000 -0.0003
980 0.2900 nan 0.1000 -0.0010
1000 0.2859 nan 0.1000 -0.0007
1020 0.2839 nan 0.1000 -0.0013
1040 0.2804 nan 0.1000 -0.0012
1060 0.2755 nan 0.1000 -0.0014
1080 0.2715 nan 0.1000 -0.0005
1100 0.2689 nan 0.1000 -0.0012
- Fold09.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3255 nan 0.0100 0.0026
2 1.3198 nan 0.0100 0.0026
3 1.3141 nan 0.0100 0.0027
4 1.3086 nan 0.0100 0.0027
5 1.3031 nan 0.0100 0.0026
6 1.2982 nan 0.0100 0.0025
7 1.2933 nan 0.0100 0.0025
8 1.2882 nan 0.0100 0.0025
9 1.2829 nan 0.0100 0.0024
10 1.2784 nan 0.0100 0.0024
20 1.2364 nan 0.0100 0.0019
40 1.1723 nan 0.0100 0.0014
60 1.1258 nan 0.0100 0.0010
80 1.0895 nan 0.0100 0.0008
100 1.0590 nan 0.0100 0.0006
120 1.0342 nan 0.0100 0.0005
140 1.0134 nan 0.0100 0.0002
160 0.9960 nan 0.0100 0.0003
180 0.9806 nan 0.0100 0.0002
200 0.9673 nan 0.0100 0.0002
220 0.9555 nan 0.0100 0.0001
240 0.9454 nan 0.0100 0.0001
260 0.9360 nan 0.0100 0.0001
280 0.9273 nan 0.0100 0.0001
300 0.9195 nan 0.0100 -0.0000
320 0.9120 nan 0.0100 0.0000
340 0.9055 nan 0.0100 0.0002
360 0.8995 nan 0.0100 0.0000
380 0.8941 nan 0.0100 0.0001
400 0.8894 nan 0.0100 0.0001
420 0.8848 nan 0.0100 -0.0000
440 0.8800 nan 0.0100 0.0001
460 0.8755 nan 0.0100 0.0001
480 0.8714 nan 0.0100 -0.0000
500 0.8671 nan 0.0100 0.0000
520 0.8632 nan 0.0100 -0.0000
540 0.8595 nan 0.0100 -0.0000
560 0.8563 nan 0.0100 -0.0000
580 0.8531 nan 0.0100 -0.0000
600 0.8501 nan 0.0100 -0.0001
620 0.8471 nan 0.0100 -0.0000
640 0.8440 nan 0.0100 -0.0000
660 0.8414 nan 0.0100 0.0000
680 0.8386 nan 0.0100 0.0000
700 0.8361 nan 0.0100 0.0000
720 0.8339 nan 0.0100 0.0000
740 0.8315 nan 0.0100 -0.0000
760 0.8291 nan 0.0100 0.0000
780 0.8269 nan 0.0100 -0.0001
800 0.8246 nan 0.0100 -0.0000
820 0.8222 nan 0.0100 0.0000
840 0.8201 nan 0.0100 0.0000
860 0.8182 nan 0.0100 0.0000
880 0.8161 nan 0.0100 -0.0000
900 0.8142 nan 0.0100 -0.0000
920 0.8123 nan 0.0100 0.0000
940 0.8108 nan 0.0100 0.0000
960 0.8090 nan 0.0100 -0.0000
980 0.8073 nan 0.0100 0.0000
1000 0.8055 nan 0.0100 -0.0001
1020 0.8042 nan 0.0100 -0.0000
1040 0.8027 nan 0.0100 0.0000
1060 0.8014 nan 0.0100 -0.0001
1080 0.7997 nan 0.0100 -0.0001
1100 0.7984 nan 0.0100 -0.0001
- Fold10.Rep2: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0038
2 1.3169 nan 0.0100 0.0036
3 1.3100 nan 0.0100 0.0035
4 1.3032 nan 0.0100 0.0033
5 1.2965 nan 0.0100 0.0033
6 1.2904 nan 0.0100 0.0033
7 1.2843 nan 0.0100 0.0032
8 1.2780 nan 0.0100 0.0031
9 1.2716 nan 0.0100 0.0030
10 1.2652 nan 0.0100 0.0029
20 1.2096 nan 0.0100 0.0022
40 1.1249 nan 0.0100 0.0016
60 1.0619 nan 0.0100 0.0012
80 1.0159 nan 0.0100 0.0009
100 0.9803 nan 0.0100 0.0007
120 0.9510 nan 0.0100 0.0004
140 0.9289 nan 0.0100 0.0001
160 0.9104 nan 0.0100 0.0003
180 0.8952 nan 0.0100 0.0001
200 0.8818 nan 0.0100 0.0001
220 0.8696 nan 0.0100 0.0001
240 0.8599 nan 0.0100 0.0001
260 0.8505 nan 0.0100 0.0000
280 0.8425 nan 0.0100 0.0001
300 0.8343 nan 0.0100 0.0001
320 0.8279 nan 0.0100 0.0000
340 0.8210 nan 0.0100 0.0000
360 0.8147 nan 0.0100 0.0002
380 0.8091 nan 0.0100 0.0000
400 0.8035 nan 0.0100 -0.0000
420 0.7981 nan 0.0100 0.0001
440 0.7935 nan 0.0100 -0.0001
460 0.7894 nan 0.0100 -0.0001
480 0.7849 nan 0.0100 -0.0001
500 0.7806 nan 0.0100 -0.0002
520 0.7770 nan 0.0100 -0.0001
540 0.7733 nan 0.0100 -0.0000
560 0.7695 nan 0.0100 -0.0000
580 0.7660 nan 0.0100 -0.0001
600 0.7628 nan 0.0100 -0.0001
620 0.7598 nan 0.0100 -0.0000
640 0.7569 nan 0.0100 0.0000
660 0.7539 nan 0.0100 -0.0001
680 0.7512 nan 0.0100 0.0000
700 0.7484 nan 0.0100 -0.0000
720 0.7456 nan 0.0100 -0.0000
740 0.7430 nan 0.0100 -0.0001
760 0.7404 nan 0.0100 -0.0001
780 0.7380 nan 0.0100 0.0000
800 0.7357 nan 0.0100 -0.0001
820 0.7337 nan 0.0100 -0.0001
840 0.7314 nan 0.0100 -0.0001
860 0.7293 nan 0.0100 -0.0001
880 0.7273 nan 0.0100 -0.0000
900 0.7250 nan 0.0100 -0.0000
920 0.7225 nan 0.0100 -0.0000
940 0.7205 nan 0.0100 -0.0001
960 0.7186 nan 0.0100 -0.0001
980 0.7169 nan 0.0100 -0.0001
1000 0.7147 nan 0.0100 -0.0001
1020 0.7126 nan 0.0100 -0.0001
1040 0.7106 nan 0.0100 -0.0001
1060 0.7086 nan 0.0100 -0.0000
1080 0.7070 nan 0.0100 0.0000
1100 0.7050 nan 0.0100 -0.0001
- Fold10.Rep2: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3232 nan 0.0100 0.0038
2 1.3148 nan 0.0100 0.0040
3 1.3072 nan 0.0100 0.0037
4 1.2994 nan 0.0100 0.0038
5 1.2923 nan 0.0100 0.0036
6 1.2852 nan 0.0100 0.0035
7 1.2780 nan 0.0100 0.0032
8 1.2707 nan 0.0100 0.0031
9 1.2643 nan 0.0100 0.0034
10 1.2580 nan 0.0100 0.0031
20 1.1970 nan 0.0100 0.0026
40 1.1027 nan 0.0100 0.0020
60 1.0347 nan 0.0100 0.0012
80 0.9835 nan 0.0100 0.0009
100 0.9452 nan 0.0100 0.0007
120 0.9161 nan 0.0100 0.0007
140 0.8931 nan 0.0100 0.0004
160 0.8739 nan 0.0100 0.0002
180 0.8573 nan 0.0100 0.0002
200 0.8416 nan 0.0100 0.0002
220 0.8293 nan 0.0100 -0.0001
240 0.8180 nan 0.0100 0.0000
260 0.8085 nan 0.0100 0.0001
280 0.7997 nan 0.0100 0.0001
300 0.7910 nan 0.0100 0.0001
320 0.7840 nan 0.0100 0.0001
340 0.7767 nan 0.0100 0.0000
360 0.7695 nan 0.0100 -0.0000
380 0.7631 nan 0.0100 -0.0000
400 0.7572 nan 0.0100 -0.0001
420 0.7524 nan 0.0100 0.0000
440 0.7470 nan 0.0100 -0.0000
460 0.7418 nan 0.0100 -0.0002
480 0.7371 nan 0.0100 -0.0002
500 0.7329 nan 0.0100 -0.0001
520 0.7284 nan 0.0100 -0.0001
540 0.7241 nan 0.0100 -0.0001
560 0.7203 nan 0.0100 -0.0000
580 0.7165 nan 0.0100 -0.0001
600 0.7130 nan 0.0100 -0.0000
620 0.7095 nan 0.0100 -0.0000
640 0.7065 nan 0.0100 -0.0001
660 0.7027 nan 0.0100 -0.0000
680 0.6990 nan 0.0100 -0.0000
700 0.6958 nan 0.0100 0.0000
720 0.6924 nan 0.0100 -0.0002
740 0.6895 nan 0.0100 -0.0002
760 0.6868 nan 0.0100 -0.0001
780 0.6835 nan 0.0100 -0.0001
800 0.6805 nan 0.0100 -0.0001
820 0.6772 nan 0.0100 -0.0001
840 0.6748 nan 0.0100 -0.0001
860 0.6721 nan 0.0100 -0.0001
880 0.6694 nan 0.0100 -0.0001
900 0.6665 nan 0.0100 -0.0001
920 0.6631 nan 0.0100 -0.0001
940 0.6607 nan 0.0100 -0.0003
960 0.6579 nan 0.0100 -0.0002
980 0.6552 nan 0.0100 -0.0002
1000 0.6527 nan 0.0100 -0.0000
1020 0.6503 nan 0.0100 -0.0000
1040 0.6477 nan 0.0100 -0.0001
1060 0.6448 nan 0.0100 -0.0001
1080 0.6427 nan 0.0100 -0.0002
1100 0.6397 nan 0.0100 -0.0002
- Fold10.Rep2: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2751 nan 0.1000 0.0271
2 1.2310 nan 0.1000 0.0214
3 1.1965 nan 0.1000 0.0177
4 1.1645 nan 0.1000 0.0147
5 1.1412 nan 0.1000 0.0125
6 1.1190 nan 0.1000 0.0098
7 1.1028 nan 0.1000 0.0085
8 1.0846 nan 0.1000 0.0084
9 1.0691 nan 0.1000 0.0066
10 1.0557 nan 0.1000 0.0065
20 0.9679 nan 0.1000 0.0022
40 0.8891 nan 0.1000 0.0003
60 0.8470 nan 0.1000 -0.0002
80 0.8208 nan 0.1000 -0.0002
100 0.8039 nan 0.1000 -0.0012
120 0.7884 nan 0.1000 0.0002
140 0.7783 nan 0.1000 -0.0008
160 0.7692 nan 0.1000 -0.0004
180 0.7618 nan 0.1000 -0.0004
200 0.7550 nan 0.1000 -0.0005
220 0.7500 nan 0.1000 -0.0009
240 0.7459 nan 0.1000 -0.0007
260 0.7405 nan 0.1000 -0.0007
280 0.7364 nan 0.1000 -0.0009
300 0.7329 nan 0.1000 -0.0005
320 0.7281 nan 0.1000 -0.0004
340 0.7251 nan 0.1000 -0.0012
360 0.7220 nan 0.1000 -0.0008
380 0.7203 nan 0.1000 -0.0008
400 0.7168 nan 0.1000 -0.0006
420 0.7128 nan 0.1000 -0.0004
440 0.7107 nan 0.1000 -0.0013
460 0.7090 nan 0.1000 -0.0021
480 0.7056 nan 0.1000 -0.0012
500 0.7010 nan 0.1000 -0.0001
520 0.6982 nan 0.1000 -0.0009
540 0.6948 nan 0.1000 -0.0005
560 0.6926 nan 0.1000 -0.0005
580 0.6905 nan 0.1000 -0.0015
600 0.6879 nan 0.1000 -0.0006
620 0.6867 nan 0.1000 -0.0012
640 0.6850 nan 0.1000 -0.0016
660 0.6819 nan 0.1000 -0.0008
680 0.6800 nan 0.1000 -0.0005
700 0.6782 nan 0.1000 -0.0011
720 0.6769 nan 0.1000 -0.0004
740 0.6758 nan 0.1000 -0.0004
760 0.6743 nan 0.1000 -0.0002
780 0.6716 nan 0.1000 -0.0006
800 0.6711 nan 0.1000 -0.0009
820 0.6700 nan 0.1000 -0.0006
840 0.6683 nan 0.1000 -0.0022
860 0.6663 nan 0.1000 -0.0021
880 0.6655 nan 0.1000 -0.0006
900 0.6646 nan 0.1000 -0.0007
920 0.6617 nan 0.1000 -0.0007
940 0.6600 nan 0.1000 -0.0009
960 0.6588 nan 0.1000 -0.0015
980 0.6563 nan 0.1000 -0.0006
1000 0.6556 nan 0.1000 -0.0014
1020 0.6527 nan 0.1000 -0.0009
1040 0.6510 nan 0.1000 -0.0006
1060 0.6490 nan 0.1000 -0.0009
1080 0.6474 nan 0.1000 -0.0008
1100 0.6457 nan 0.1000 -0.0004
- Fold10.Rep2: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2617 nan 0.1000 0.0313
2 1.2039 nan 0.1000 0.0276
3 1.1585 nan 0.1000 0.0229
4 1.1201 nan 0.1000 0.0198
5 1.0859 nan 0.1000 0.0160
6 1.0581 nan 0.1000 0.0124
7 1.0358 nan 0.1000 0.0108
8 1.0169 nan 0.1000 0.0074
9 0.9955 nan 0.1000 0.0091
10 0.9781 nan 0.1000 0.0075
20 0.8837 nan 0.1000 0.0003
40 0.8023 nan 0.1000 0.0012
60 0.7714 nan 0.1000 -0.0008
80 0.7459 nan 0.1000 -0.0006
100 0.7192 nan 0.1000 -0.0002
120 0.6992 nan 0.1000 -0.0007
140 0.6853 nan 0.1000 -0.0017
160 0.6724 nan 0.1000 -0.0017
180 0.6565 nan 0.1000 -0.0016
200 0.6432 nan 0.1000 -0.0016
220 0.6346 nan 0.1000 -0.0017
240 0.6259 nan 0.1000 -0.0013
260 0.6181 nan 0.1000 -0.0012
280 0.6061 nan 0.1000 -0.0004
300 0.5964 nan 0.1000 -0.0015
320 0.5892 nan 0.1000 -0.0006
340 0.5793 nan 0.1000 -0.0013
360 0.5712 nan 0.1000 -0.0012
380 0.5662 nan 0.1000 -0.0018
400 0.5576 nan 0.1000 -0.0009
420 0.5512 nan 0.1000 -0.0006
440 0.5413 nan 0.1000 -0.0011
460 0.5355 nan 0.1000 -0.0008
480 0.5290 nan 0.1000 -0.0017
500 0.5206 nan 0.1000 -0.0011
520 0.5135 nan 0.1000 -0.0006
540 0.5079 nan 0.1000 -0.0011
560 0.5033 nan 0.1000 -0.0010
580 0.4999 nan 0.1000 -0.0015
600 0.4945 nan 0.1000 -0.0008
620 0.4887 nan 0.1000 -0.0007
640 0.4824 nan 0.1000 -0.0005
660 0.4770 nan 0.1000 -0.0013
680 0.4710 nan 0.1000 -0.0006
700 0.4649 nan 0.1000 -0.0016
720 0.4604 nan 0.1000 -0.0012
740 0.4568 nan 0.1000 -0.0009
760 0.4519 nan 0.1000 -0.0009
780 0.4472 nan 0.1000 -0.0006
800 0.4430 nan 0.1000 -0.0007
820 0.4372 nan 0.1000 -0.0006
840 0.4313 nan 0.1000 -0.0007
860 0.4284 nan 0.1000 -0.0007
880 0.4259 nan 0.1000 -0.0006
900 0.4235 nan 0.1000 -0.0006
920 0.4201 nan 0.1000 -0.0007
940 0.4154 nan 0.1000 -0.0008
960 0.4116 nan 0.1000 -0.0009
980 0.4074 nan 0.1000 -0.0006
1000 0.4039 nan 0.1000 -0.0009
1020 0.3994 nan 0.1000 -0.0012
1040 0.3954 nan 0.1000 -0.0010
1060 0.3932 nan 0.1000 -0.0007
1080 0.3910 nan 0.1000 -0.0009
1100 0.3891 nan 0.1000 -0.0013
- Fold10.Rep2: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2562 nan 0.1000 0.0363
2 1.1954 nan 0.1000 0.0293
3 1.1489 nan 0.1000 0.0206
4 1.1056 nan 0.1000 0.0217
5 1.0692 nan 0.1000 0.0184
6 1.0369 nan 0.1000 0.0150
7 1.0082 nan 0.1000 0.0127
8 0.9862 nan 0.1000 0.0098
9 0.9707 nan 0.1000 0.0076
10 0.9531 nan 0.1000 0.0057
20 0.8454 nan 0.1000 -0.0003
40 0.7668 nan 0.1000 -0.0007
60 0.7203 nan 0.1000 -0.0014
80 0.6886 nan 0.1000 -0.0007
100 0.6627 nan 0.1000 -0.0005
120 0.6416 nan 0.1000 -0.0003
140 0.6216 nan 0.1000 -0.0017
160 0.6033 nan 0.1000 -0.0013
180 0.5785 nan 0.1000 -0.0008
200 0.5646 nan 0.1000 -0.0007
220 0.5456 nan 0.1000 -0.0011
240 0.5301 nan 0.1000 -0.0005
260 0.5197 nan 0.1000 -0.0008
280 0.5092 nan 0.1000 -0.0011
300 0.4961 nan 0.1000 -0.0009
320 0.4844 nan 0.1000 -0.0022
340 0.4745 nan 0.1000 -0.0016
360 0.4627 nan 0.1000 -0.0010
380 0.4543 nan 0.1000 -0.0021
400 0.4433 nan 0.1000 -0.0007
420 0.4349 nan 0.1000 -0.0019
440 0.4282 nan 0.1000 -0.0015
460 0.4210 nan 0.1000 -0.0007
480 0.4142 nan 0.1000 -0.0015
500 0.4058 nan 0.1000 -0.0015
520 0.3963 nan 0.1000 -0.0009
540 0.3901 nan 0.1000 -0.0012
560 0.3827 nan 0.1000 -0.0005
580 0.3759 nan 0.1000 -0.0007
600 0.3678 nan 0.1000 -0.0001
620 0.3619 nan 0.1000 -0.0014
640 0.3538 nan 0.1000 -0.0010
660 0.3489 nan 0.1000 -0.0006
680 0.3449 nan 0.1000 -0.0016
700 0.3396 nan 0.1000 -0.0011
720 0.3318 nan 0.1000 -0.0005
740 0.3271 nan 0.1000 -0.0014
760 0.3227 nan 0.1000 -0.0012
780 0.3183 nan 0.1000 -0.0015
800 0.3137 nan 0.1000 -0.0007
820 0.3084 nan 0.1000 -0.0008
840 0.3039 nan 0.1000 -0.0006
860 0.3008 nan 0.1000 -0.0008
880 0.2953 nan 0.1000 -0.0006
900 0.2916 nan 0.1000 -0.0010
920 0.2877 nan 0.1000 -0.0010
940 0.2831 nan 0.1000 -0.0004
960 0.2790 nan 0.1000 -0.0013
980 0.2758 nan 0.1000 -0.0016
1000 0.2719 nan 0.1000 -0.0010
1020 0.2675 nan 0.1000 -0.0008
1040 0.2638 nan 0.1000 -0.0008
1060 0.2600 nan 0.1000 -0.0005
1080 0.2562 nan 0.1000 -0.0010
1100 0.2538 nan 0.1000 -0.0008
- Fold10.Rep2: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3247 nan 0.0100 0.0033
2 1.3180 nan 0.0100 0.0030
3 1.3118 nan 0.0100 0.0031
4 1.3056 nan 0.0100 0.0029
5 1.2995 nan 0.0100 0.0029
6 1.2937 nan 0.0100 0.0030
7 1.2879 nan 0.0100 0.0029
8 1.2821 nan 0.0100 0.0027
9 1.2767 nan 0.0100 0.0028
10 1.2712 nan 0.0100 0.0027
20 1.2209 nan 0.0100 0.0022
40 1.1446 nan 0.0100 0.0015
60 1.0920 nan 0.0100 0.0007
80 1.0542 nan 0.0100 0.0008
100 1.0234 nan 0.0100 0.0005
120 0.9984 nan 0.0100 0.0005
140 0.9775 nan 0.0100 0.0004
160 0.9589 nan 0.0100 0.0003
180 0.9446 nan 0.0100 0.0002
200 0.9308 nan 0.0100 0.0001
220 0.9192 nan 0.0100 0.0002
240 0.9087 nan 0.0100 0.0001
260 0.8993 nan 0.0100 0.0002
280 0.8912 nan 0.0100 0.0001
300 0.8834 nan 0.0100 0.0002
320 0.8764 nan 0.0100 0.0000
340 0.8699 nan 0.0100 0.0001
360 0.8645 nan 0.0100 0.0001
380 0.8593 nan 0.0100 -0.0001
400 0.8543 nan 0.0100 0.0001
420 0.8500 nan 0.0100 -0.0001
440 0.8456 nan 0.0100 -0.0001
460 0.8416 nan 0.0100 0.0001
480 0.8376 nan 0.0100 0.0000
500 0.8339 nan 0.0100 0.0000
520 0.8304 nan 0.0100 -0.0000
540 0.8270 nan 0.0100 -0.0002
560 0.8240 nan 0.0100 0.0000
580 0.8209 nan 0.0100 0.0000
600 0.8180 nan 0.0100 -0.0000
620 0.8152 nan 0.0100 -0.0000
640 0.8127 nan 0.0100 0.0000
660 0.8102 nan 0.0100 0.0000
680 0.8079 nan 0.0100 -0.0001
700 0.8058 nan 0.0100 0.0000
720 0.8038 nan 0.0100 -0.0001
740 0.8019 nan 0.0100 0.0000
760 0.7998 nan 0.0100 -0.0000
780 0.7981 nan 0.0100 -0.0000
800 0.7963 nan 0.0100 -0.0000
820 0.7946 nan 0.0100 -0.0001
840 0.7928 nan 0.0100 -0.0001
860 0.7910 nan 0.0100 0.0000
880 0.7892 nan 0.0100 0.0000
900 0.7876 nan 0.0100 -0.0000
920 0.7861 nan 0.0100 -0.0001
940 0.7846 nan 0.0100 -0.0000
960 0.7831 nan 0.0100 -0.0000
980 0.7817 nan 0.0100 -0.0001
1000 0.7802 nan 0.0100 -0.0000
1020 0.7790 nan 0.0100 -0.0001
1040 0.7778 nan 0.0100 -0.0001
1060 0.7767 nan 0.0100 -0.0001
1080 0.7756 nan 0.0100 -0.0000
1100 0.7744 nan 0.0100 -0.0000
- Fold01.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0037
2 1.3163 nan 0.0100 0.0039
3 1.3083 nan 0.0100 0.0037
4 1.3007 nan 0.0100 0.0038
5 1.2930 nan 0.0100 0.0039
6 1.2858 nan 0.0100 0.0036
7 1.2783 nan 0.0100 0.0033
8 1.2713 nan 0.0100 0.0029
9 1.2646 nan 0.0100 0.0034
10 1.2575 nan 0.0100 0.0033
20 1.1973 nan 0.0100 0.0028
40 1.1035 nan 0.0100 0.0020
60 1.0357 nan 0.0100 0.0014
80 0.9858 nan 0.0100 0.0010
100 0.9471 nan 0.0100 0.0007
120 0.9170 nan 0.0100 0.0006
140 0.8925 nan 0.0100 0.0004
160 0.8749 nan 0.0100 0.0003
180 0.8599 nan 0.0100 0.0002
200 0.8474 nan 0.0100 0.0002
220 0.8360 nan 0.0100 0.0002
240 0.8261 nan 0.0100 0.0001
260 0.8174 nan 0.0100 0.0001
280 0.8101 nan 0.0100 0.0000
300 0.8035 nan 0.0100 0.0000
320 0.7970 nan 0.0100 -0.0000
340 0.7911 nan 0.0100 0.0001
360 0.7855 nan 0.0100 -0.0000
380 0.7798 nan 0.0100 0.0001
400 0.7744 nan 0.0100 0.0001
420 0.7700 nan 0.0100 0.0000
440 0.7657 nan 0.0100 -0.0001
460 0.7615 nan 0.0100 -0.0000
480 0.7576 nan 0.0100 -0.0000
500 0.7542 nan 0.0100 0.0000
520 0.7510 nan 0.0100 -0.0001
540 0.7477 nan 0.0100 -0.0001
560 0.7446 nan 0.0100 -0.0000
580 0.7411 nan 0.0100 -0.0000
600 0.7383 nan 0.0100 -0.0000
620 0.7352 nan 0.0100 -0.0000
640 0.7324 nan 0.0100 0.0000
660 0.7299 nan 0.0100 -0.0001
680 0.7273 nan 0.0100 -0.0000
700 0.7246 nan 0.0100 -0.0002
720 0.7221 nan 0.0100 -0.0001
740 0.7194 nan 0.0100 -0.0002
760 0.7172 nan 0.0100 -0.0000
780 0.7150 nan 0.0100 -0.0001
800 0.7127 nan 0.0100 -0.0002
820 0.7106 nan 0.0100 -0.0000
840 0.7088 nan 0.0100 -0.0002
860 0.7066 nan 0.0100 -0.0001
880 0.7047 nan 0.0100 -0.0000
900 0.7027 nan 0.0100 -0.0001
920 0.7006 nan 0.0100 -0.0002
940 0.6989 nan 0.0100 -0.0001
960 0.6967 nan 0.0100 -0.0000
980 0.6948 nan 0.0100 -0.0001
1000 0.6928 nan 0.0100 -0.0001
1020 0.6912 nan 0.0100 -0.0001
1040 0.6895 nan 0.0100 -0.0001
1060 0.6879 nan 0.0100 -0.0001
1080 0.6862 nan 0.0100 -0.0001
1100 0.6843 nan 0.0100 -0.0001
- Fold01.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3235 nan 0.0100 0.0040
2 1.3151 nan 0.0100 0.0039
3 1.3066 nan 0.0100 0.0040
4 1.2987 nan 0.0100 0.0037
5 1.2907 nan 0.0100 0.0041
6 1.2826 nan 0.0100 0.0038
7 1.2754 nan 0.0100 0.0036
8 1.2677 nan 0.0100 0.0034
9 1.2601 nan 0.0100 0.0036
10 1.2533 nan 0.0100 0.0035
20 1.1874 nan 0.0100 0.0028
40 1.0840 nan 0.0100 0.0021
60 1.0112 nan 0.0100 0.0016
80 0.9561 nan 0.0100 0.0010
100 0.9150 nan 0.0100 0.0004
120 0.8834 nan 0.0100 0.0004
140 0.8584 nan 0.0100 0.0004
160 0.8384 nan 0.0100 0.0004
180 0.8224 nan 0.0100 0.0003
200 0.8093 nan 0.0100 0.0001
220 0.7970 nan 0.0100 0.0001
240 0.7870 nan 0.0100 -0.0001
260 0.7782 nan 0.0100 0.0002
280 0.7695 nan 0.0100 -0.0001
300 0.7618 nan 0.0100 0.0001
320 0.7548 nan 0.0100 0.0001
340 0.7480 nan 0.0100 -0.0001
360 0.7423 nan 0.0100 0.0000
380 0.7372 nan 0.0100 -0.0000
400 0.7317 nan 0.0100 -0.0000
420 0.7266 nan 0.0100 -0.0001
440 0.7219 nan 0.0100 -0.0001
460 0.7172 nan 0.0100 -0.0001
480 0.7128 nan 0.0100 -0.0000
500 0.7093 nan 0.0100 -0.0000
520 0.7051 nan 0.0100 0.0000
540 0.7013 nan 0.0100 -0.0001
560 0.6981 nan 0.0100 -0.0001
580 0.6950 nan 0.0100 -0.0001
600 0.6919 nan 0.0100 -0.0001
620 0.6882 nan 0.0100 -0.0002
640 0.6847 nan 0.0100 -0.0000
660 0.6816 nan 0.0100 -0.0000
680 0.6784 nan 0.0100 -0.0001
700 0.6752 nan 0.0100 -0.0001
720 0.6721 nan 0.0100 -0.0001
740 0.6694 nan 0.0100 0.0000
760 0.6659 nan 0.0100 -0.0001
780 0.6630 nan 0.0100 -0.0002
800 0.6598 nan 0.0100 -0.0002
820 0.6572 nan 0.0100 -0.0001
840 0.6546 nan 0.0100 -0.0003
860 0.6521 nan 0.0100 -0.0001
880 0.6492 nan 0.0100 -0.0001
900 0.6466 nan 0.0100 -0.0001
920 0.6437 nan 0.0100 -0.0001
940 0.6413 nan 0.0100 -0.0001
960 0.6387 nan 0.0100 -0.0001
980 0.6364 nan 0.0100 -0.0001
1000 0.6341 nan 0.0100 -0.0001
1020 0.6314 nan 0.0100 -0.0001
1040 0.6290 nan 0.0100 -0.0001
1060 0.6264 nan 0.0100 -0.0001
1080 0.6241 nan 0.0100 -0.0001
1100 0.6214 nan 0.0100 -0.0002
- Fold01.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2655 nan 0.1000 0.0317
2 1.2187 nan 0.1000 0.0245
3 1.1798 nan 0.1000 0.0206
4 1.1425 nan 0.1000 0.0173
5 1.1135 nan 0.1000 0.0142
6 1.0905 nan 0.1000 0.0115
7 1.0710 nan 0.1000 0.0094
8 1.0558 nan 0.1000 0.0064
9 1.0378 nan 0.1000 0.0072
10 1.0235 nan 0.1000 0.0060
20 0.9317 nan 0.1000 0.0012
40 0.8571 nan 0.1000 -0.0006
60 0.8199 nan 0.1000 0.0005
80 0.7966 nan 0.1000 -0.0001
100 0.7802 nan 0.1000 -0.0011
120 0.7690 nan 0.1000 -0.0004
140 0.7619 nan 0.1000 -0.0011
160 0.7520 nan 0.1000 -0.0004
180 0.7465 nan 0.1000 -0.0012
200 0.7404 nan 0.1000 -0.0002
220 0.7347 nan 0.1000 -0.0012
240 0.7289 nan 0.1000 -0.0007
260 0.7249 nan 0.1000 -0.0005
280 0.7207 nan 0.1000 -0.0011
300 0.7161 nan 0.1000 -0.0005
320 0.7131 nan 0.1000 -0.0004
340 0.7101 nan 0.1000 -0.0010
360 0.7059 nan 0.1000 -0.0005
380 0.7025 nan 0.1000 -0.0013
400 0.6995 nan 0.1000 -0.0007
420 0.6976 nan 0.1000 -0.0004
440 0.6947 nan 0.1000 -0.0006
460 0.6926 nan 0.1000 -0.0006
480 0.6899 nan 0.1000 -0.0010
500 0.6884 nan 0.1000 -0.0008
520 0.6871 nan 0.1000 -0.0013
540 0.6832 nan 0.1000 -0.0009
560 0.6814 nan 0.1000 -0.0012
580 0.6787 nan 0.1000 -0.0009
600 0.6759 nan 0.1000 -0.0007
620 0.6758 nan 0.1000 -0.0009
640 0.6735 nan 0.1000 -0.0007
660 0.6714 nan 0.1000 -0.0004
680 0.6703 nan 0.1000 -0.0007
700 0.6680 nan 0.1000 -0.0013
720 0.6663 nan 0.1000 -0.0005
740 0.6639 nan 0.1000 -0.0012
760 0.6617 nan 0.1000 -0.0006
780 0.6602 nan 0.1000 -0.0008
800 0.6599 nan 0.1000 -0.0012
820 0.6586 nan 0.1000 -0.0009
840 0.6569 nan 0.1000 -0.0005
860 0.6559 nan 0.1000 -0.0005
880 0.6540 nan 0.1000 -0.0004
900 0.6512 nan 0.1000 -0.0005
920 0.6513 nan 0.1000 -0.0007
940 0.6474 nan 0.1000 -0.0015
960 0.6464 nan 0.1000 -0.0005
980 0.6457 nan 0.1000 -0.0009
1000 0.6455 nan 0.1000 -0.0005
1020 0.6428 nan 0.1000 -0.0013
1040 0.6407 nan 0.1000 -0.0010
1060 0.6400 nan 0.1000 -0.0011
1080 0.6386 nan 0.1000 -0.0007
1100 0.6381 nan 0.1000 -0.0004
- Fold01.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2587 nan 0.1000 0.0351
2 1.1947 nan 0.1000 0.0317
3 1.1428 nan 0.1000 0.0251
4 1.0988 nan 0.1000 0.0212
5 1.0635 nan 0.1000 0.0196
6 1.0321 nan 0.1000 0.0157
7 1.0080 nan 0.1000 0.0104
8 0.9850 nan 0.1000 0.0106
9 0.9670 nan 0.1000 0.0078
10 0.9465 nan 0.1000 0.0086
20 0.8423 nan 0.1000 0.0025
40 0.7784 nan 0.1000 -0.0006
60 0.7365 nan 0.1000 0.0000
80 0.7183 nan 0.1000 -0.0005
100 0.7010 nan 0.1000 -0.0015
120 0.6843 nan 0.1000 -0.0015
140 0.6634 nan 0.1000 -0.0003
160 0.6536 nan 0.1000 -0.0012
180 0.6405 nan 0.1000 -0.0002
200 0.6258 nan 0.1000 -0.0018
220 0.6148 nan 0.1000 -0.0004
240 0.6062 nan 0.1000 -0.0000
260 0.5957 nan 0.1000 -0.0010
280 0.5850 nan 0.1000 -0.0006
300 0.5751 nan 0.1000 -0.0010
320 0.5649 nan 0.1000 -0.0007
340 0.5577 nan 0.1000 -0.0008
360 0.5497 nan 0.1000 -0.0010
380 0.5428 nan 0.1000 -0.0015
400 0.5361 nan 0.1000 -0.0008
420 0.5286 nan 0.1000 -0.0011
440 0.5220 nan 0.1000 -0.0008
460 0.5155 nan 0.1000 -0.0004
480 0.5091 nan 0.1000 -0.0002
500 0.5023 nan 0.1000 -0.0010
520 0.4952 nan 0.1000 -0.0017
540 0.4899 nan 0.1000 -0.0010
560 0.4853 nan 0.1000 -0.0015
580 0.4799 nan 0.1000 -0.0009
600 0.4758 nan 0.1000 -0.0014
620 0.4723 nan 0.1000 -0.0011
640 0.4655 nan 0.1000 -0.0012
660 0.4601 nan 0.1000 -0.0005
680 0.4543 nan 0.1000 -0.0003
700 0.4513 nan 0.1000 -0.0009
720 0.4481 nan 0.1000 -0.0003
740 0.4435 nan 0.1000 -0.0012
760 0.4377 nan 0.1000 -0.0007
780 0.4341 nan 0.1000 -0.0008
800 0.4301 nan 0.1000 -0.0009
820 0.4270 nan 0.1000 -0.0005
840 0.4243 nan 0.1000 -0.0008
860 0.4199 nan 0.1000 -0.0013
880 0.4165 nan 0.1000 -0.0008
900 0.4121 nan 0.1000 -0.0012
920 0.4092 nan 0.1000 -0.0009
940 0.4054 nan 0.1000 -0.0008
960 0.4023 nan 0.1000 -0.0006
980 0.3983 nan 0.1000 -0.0013
1000 0.3969 nan 0.1000 -0.0006
1020 0.3944 nan 0.1000 -0.0008
1040 0.3912 nan 0.1000 -0.0008
1060 0.3891 nan 0.1000 -0.0006
1080 0.3867 nan 0.1000 -0.0007
1100 0.3834 nan 0.1000 -0.0006
- Fold01.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2471 nan 0.1000 0.0425
2 1.1819 nan 0.1000 0.0344
3 1.1269 nan 0.1000 0.0242
4 1.0810 nan 0.1000 0.0202
5 1.0394 nan 0.1000 0.0201
6 1.0059 nan 0.1000 0.0160
7 0.9779 nan 0.1000 0.0145
8 0.9553 nan 0.1000 0.0107
9 0.9334 nan 0.1000 0.0102
10 0.9132 nan 0.1000 0.0076
20 0.8140 nan 0.1000 0.0029
40 0.7387 nan 0.1000 0.0000
60 0.6941 nan 0.1000 -0.0011
80 0.6639 nan 0.1000 -0.0013
100 0.6393 nan 0.1000 -0.0007
120 0.6128 nan 0.1000 -0.0013
140 0.5923 nan 0.1000 -0.0008
160 0.5728 nan 0.1000 -0.0011
180 0.5574 nan 0.1000 -0.0015
200 0.5435 nan 0.1000 -0.0010
220 0.5294 nan 0.1000 -0.0005
240 0.5178 nan 0.1000 -0.0011
260 0.5089 nan 0.1000 -0.0007
280 0.4972 nan 0.1000 -0.0014
300 0.4856 nan 0.1000 -0.0009
320 0.4746 nan 0.1000 -0.0008
340 0.4651 nan 0.1000 -0.0010
360 0.4554 nan 0.1000 -0.0008
380 0.4443 nan 0.1000 -0.0008
400 0.4365 nan 0.1000 -0.0010
420 0.4285 nan 0.1000 -0.0009
440 0.4194 nan 0.1000 -0.0008
460 0.4118 nan 0.1000 -0.0008
480 0.4029 nan 0.1000 -0.0020
500 0.3954 nan 0.1000 -0.0016
520 0.3900 nan 0.1000 -0.0009
540 0.3819 nan 0.1000 -0.0009
560 0.3744 nan 0.1000 -0.0008
580 0.3686 nan 0.1000 -0.0014
600 0.3617 nan 0.1000 -0.0005
620 0.3568 nan 0.1000 -0.0008
640 0.3510 nan 0.1000 -0.0013
660 0.3432 nan 0.1000 -0.0004
680 0.3371 nan 0.1000 -0.0014
700 0.3334 nan 0.1000 -0.0004
720 0.3293 nan 0.1000 -0.0011
740 0.3258 nan 0.1000 -0.0009
760 0.3214 nan 0.1000 -0.0014
780 0.3164 nan 0.1000 -0.0009
800 0.3123 nan 0.1000 -0.0008
820 0.3092 nan 0.1000 -0.0007
840 0.3059 nan 0.1000 -0.0004
860 0.3020 nan 0.1000 -0.0007
880 0.2970 nan 0.1000 -0.0005
900 0.2940 nan 0.1000 -0.0006
920 0.2914 nan 0.1000 -0.0012
940 0.2874 nan 0.1000 -0.0006
960 0.2846 nan 0.1000 -0.0014
980 0.2812 nan 0.1000 -0.0006
1000 0.2755 nan 0.1000 -0.0007
1020 0.2727 nan 0.1000 -0.0008
1040 0.2688 nan 0.1000 -0.0005
1060 0.2665 nan 0.1000 -0.0016
1080 0.2649 nan 0.1000 -0.0012
1100 0.2620 nan 0.1000 -0.0010
- Fold01.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3257 nan 0.0100 0.0031
2 1.3200 nan 0.0100 0.0029
3 1.3144 nan 0.0100 0.0029
4 1.3089 nan 0.0100 0.0029
5 1.3031 nan 0.0100 0.0028
6 1.2973 nan 0.0100 0.0026
7 1.2912 nan 0.0100 0.0024
8 1.2861 nan 0.0100 0.0026
9 1.2805 nan 0.0100 0.0025
10 1.2752 nan 0.0100 0.0024
20 1.2304 nan 0.0100 0.0021
40 1.1598 nan 0.0100 0.0014
60 1.1111 nan 0.0100 0.0010
80 1.0752 nan 0.0100 0.0008
100 1.0446 nan 0.0100 0.0006
120 1.0213 nan 0.0100 0.0005
140 1.0027 nan 0.0100 0.0003
160 0.9857 nan 0.0100 0.0002
180 0.9713 nan 0.0100 0.0003
200 0.9581 nan 0.0100 0.0002
220 0.9472 nan 0.0100 0.0001
240 0.9369 nan 0.0100 0.0002
260 0.9275 nan 0.0100 0.0001
280 0.9198 nan 0.0100 0.0000
300 0.9122 nan 0.0100 0.0001
320 0.9055 nan 0.0100 0.0001
340 0.8989 nan 0.0100 0.0001
360 0.8930 nan 0.0100 0.0001
380 0.8878 nan 0.0100 0.0000
400 0.8833 nan 0.0100 0.0000
420 0.8783 nan 0.0100 0.0001
440 0.8741 nan 0.0100 -0.0001
460 0.8701 nan 0.0100 0.0000
480 0.8663 nan 0.0100 0.0000
500 0.8620 nan 0.0100 -0.0000
520 0.8581 nan 0.0100 0.0001
540 0.8550 nan 0.0100 0.0000
560 0.8520 nan 0.0100 0.0000
580 0.8488 nan 0.0100 0.0000
600 0.8455 nan 0.0100 -0.0000
620 0.8430 nan 0.0100 0.0000
640 0.8403 nan 0.0100 -0.0000
660 0.8375 nan 0.0100 -0.0000
680 0.8349 nan 0.0100 -0.0000
700 0.8321 nan 0.0100 -0.0000
720 0.8295 nan 0.0100 0.0000
740 0.8271 nan 0.0100 -0.0000
760 0.8250 nan 0.0100 -0.0001
780 0.8228 nan 0.0100 -0.0001
800 0.8207 nan 0.0100 -0.0001
820 0.8187 nan 0.0100 -0.0000
840 0.8171 nan 0.0100 -0.0000
860 0.8152 nan 0.0100 -0.0001
880 0.8131 nan 0.0100 -0.0000
900 0.8115 nan 0.0100 -0.0001
920 0.8098 nan 0.0100 -0.0001
940 0.8080 nan 0.0100 -0.0000
960 0.8065 nan 0.0100 -0.0001
980 0.8050 nan 0.0100 0.0000
1000 0.8038 nan 0.0100 -0.0000
1020 0.8024 nan 0.0100 -0.0000
1040 0.8011 nan 0.0100 -0.0001
1060 0.7997 nan 0.0100 -0.0002
1080 0.7985 nan 0.0100 -0.0001
1100 0.7971 nan 0.0100 -0.0001
- Fold02.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3245 nan 0.0100 0.0038
2 1.3171 nan 0.0100 0.0035
3 1.3102 nan 0.0100 0.0036
4 1.3032 nan 0.0100 0.0035
5 1.2962 nan 0.0100 0.0035
6 1.2893 nan 0.0100 0.0031
7 1.2825 nan 0.0100 0.0032
8 1.2758 nan 0.0100 0.0030
9 1.2693 nan 0.0100 0.0032
10 1.2631 nan 0.0100 0.0031
20 1.2046 nan 0.0100 0.0026
40 1.1163 nan 0.0100 0.0017
60 1.0524 nan 0.0100 0.0012
80 1.0045 nan 0.0100 0.0009
100 0.9680 nan 0.0100 0.0007
120 0.9391 nan 0.0100 0.0006
140 0.9177 nan 0.0100 0.0004
160 0.9000 nan 0.0100 0.0002
180 0.8851 nan 0.0100 0.0002
200 0.8727 nan 0.0100 0.0002
220 0.8620 nan 0.0100 0.0002
240 0.8528 nan 0.0100 -0.0001
260 0.8433 nan 0.0100 0.0001
280 0.8344 nan 0.0100 0.0002
300 0.8268 nan 0.0100 0.0001
320 0.8194 nan 0.0100 -0.0001
340 0.8130 nan 0.0100 0.0000
360 0.8070 nan 0.0100 0.0001
380 0.8016 nan 0.0100 0.0000
400 0.7967 nan 0.0100 0.0001
420 0.7917 nan 0.0100 -0.0001
440 0.7869 nan 0.0100 -0.0000
460 0.7825 nan 0.0100 -0.0001
480 0.7786 nan 0.0100 -0.0000
500 0.7746 nan 0.0100 -0.0001
520 0.7712 nan 0.0100 -0.0001
540 0.7680 nan 0.0100 -0.0001
560 0.7646 nan 0.0100 -0.0001
580 0.7616 nan 0.0100 -0.0001
600 0.7586 nan 0.0100 -0.0001
620 0.7559 nan 0.0100 -0.0001
640 0.7528 nan 0.0100 0.0000
660 0.7504 nan 0.0100 -0.0002
680 0.7477 nan 0.0100 -0.0001
700 0.7450 nan 0.0100 -0.0000
720 0.7427 nan 0.0100 -0.0001
740 0.7400 nan 0.0100 -0.0001
760 0.7378 nan 0.0100 -0.0000
780 0.7356 nan 0.0100 -0.0001
800 0.7329 nan 0.0100 -0.0000
820 0.7306 nan 0.0100 -0.0001
840 0.7284 nan 0.0100 -0.0000
860 0.7260 nan 0.0100 -0.0001
880 0.7240 nan 0.0100 -0.0000
900 0.7217 nan 0.0100 0.0000
920 0.7198 nan 0.0100 -0.0002
940 0.7175 nan 0.0100 -0.0001
960 0.7159 nan 0.0100 -0.0002
980 0.7138 nan 0.0100 -0.0000
1000 0.7115 nan 0.0100 -0.0001
1020 0.7099 nan 0.0100 -0.0001
1040 0.7081 nan 0.0100 -0.0000
1060 0.7062 nan 0.0100 -0.0001
1080 0.7038 nan 0.0100 -0.0001
1100 0.7019 nan 0.0100 -0.0000
- Fold02.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3239 nan 0.0100 0.0040
2 1.3158 nan 0.0100 0.0037
3 1.3077 nan 0.0100 0.0038
4 1.2992 nan 0.0100 0.0038
5 1.2915 nan 0.0100 0.0034
6 1.2833 nan 0.0100 0.0039
7 1.2763 nan 0.0100 0.0035
8 1.2689 nan 0.0100 0.0034
9 1.2616 nan 0.0100 0.0034
10 1.2548 nan 0.0100 0.0033
20 1.1916 nan 0.0100 0.0028
40 1.0962 nan 0.0100 0.0020
60 1.0269 nan 0.0100 0.0015
80 0.9742 nan 0.0100 0.0009
100 0.9350 nan 0.0100 0.0007
120 0.9040 nan 0.0100 0.0005
140 0.8811 nan 0.0100 0.0002
160 0.8625 nan 0.0100 0.0002
180 0.8460 nan 0.0100 0.0003
200 0.8320 nan 0.0100 0.0003
220 0.8192 nan 0.0100 0.0001
240 0.8087 nan 0.0100 0.0001
260 0.7994 nan 0.0100 -0.0000
280 0.7907 nan 0.0100 0.0000
300 0.7836 nan 0.0100 0.0000
320 0.7768 nan 0.0100 -0.0001
340 0.7700 nan 0.0100 -0.0000
360 0.7633 nan 0.0100 -0.0000
380 0.7566 nan 0.0100 0.0000
400 0.7512 nan 0.0100 -0.0000
420 0.7462 nan 0.0100 -0.0001
440 0.7419 nan 0.0100 -0.0000
460 0.7371 nan 0.0100 -0.0002
480 0.7327 nan 0.0100 -0.0000
500 0.7289 nan 0.0100 -0.0001
520 0.7249 nan 0.0100 -0.0001
540 0.7215 nan 0.0100 -0.0002
560 0.7168 nan 0.0100 0.0000
580 0.7134 nan 0.0100 -0.0002
600 0.7095 nan 0.0100 -0.0001
620 0.7055 nan 0.0100 -0.0000
640 0.7016 nan 0.0100 -0.0001
660 0.6979 nan 0.0100 -0.0001
680 0.6945 nan 0.0100 -0.0001
700 0.6913 nan 0.0100 -0.0001
720 0.6879 nan 0.0100 -0.0001
740 0.6850 nan 0.0100 -0.0001
760 0.6820 nan 0.0100 -0.0002
780 0.6791 nan 0.0100 -0.0001
800 0.6765 nan 0.0100 -0.0002
820 0.6736 nan 0.0100 -0.0001
840 0.6714 nan 0.0100 -0.0002
860 0.6685 nan 0.0100 -0.0001
880 0.6656 nan 0.0100 -0.0002
900 0.6634 nan 0.0100 -0.0000
920 0.6608 nan 0.0100 -0.0001
940 0.6583 nan 0.0100 -0.0001
960 0.6553 nan 0.0100 0.0000
980 0.6528 nan 0.0100 -0.0002
1000 0.6503 nan 0.0100 -0.0001
1020 0.6477 nan 0.0100 -0.0002
1040 0.6453 nan 0.0100 -0.0001
1060 0.6425 nan 0.0100 -0.0001
1080 0.6407 nan 0.0100 -0.0001
1100 0.6383 nan 0.0100 -0.0001
- Fold02.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2729 nan 0.1000 0.0272
2 1.2284 nan 0.1000 0.0237
3 1.1881 nan 0.1000 0.0188
4 1.1604 nan 0.1000 0.0162
5 1.1339 nan 0.1000 0.0124
6 1.1084 nan 0.1000 0.0114
7 1.0873 nan 0.1000 0.0091
8 1.0697 nan 0.1000 0.0073
9 1.0556 nan 0.1000 0.0067
10 1.0428 nan 0.1000 0.0051
20 0.9554 nan 0.1000 0.0008
40 0.8805 nan 0.1000 0.0007
60 0.8444 nan 0.1000 0.0002
80 0.8215 nan 0.1000 -0.0015
100 0.8042 nan 0.1000 -0.0011
120 0.7932 nan 0.1000 0.0002
140 0.7848 nan 0.1000 -0.0005
160 0.7758 nan 0.1000 -0.0014
180 0.7677 nan 0.1000 -0.0008
200 0.7618 nan 0.1000 -0.0007
220 0.7548 nan 0.1000 -0.0008
240 0.7501 nan 0.1000 -0.0004
260 0.7447 nan 0.1000 -0.0010
280 0.7392 nan 0.1000 -0.0008
300 0.7351 nan 0.1000 -0.0008
320 0.7322 nan 0.1000 -0.0010
340 0.7301 nan 0.1000 -0.0008
360 0.7258 nan 0.1000 -0.0004
380 0.7226 nan 0.1000 -0.0008
400 0.7193 nan 0.1000 -0.0007
420 0.7160 nan 0.1000 -0.0012
440 0.7112 nan 0.1000 -0.0006
460 0.7094 nan 0.1000 -0.0006
480 0.7081 nan 0.1000 -0.0019
500 0.7069 nan 0.1000 -0.0009
520 0.7038 nan 0.1000 -0.0006
540 0.7014 nan 0.1000 -0.0003
560 0.7002 nan 0.1000 -0.0008
580 0.6969 nan 0.1000 -0.0010
600 0.6952 nan 0.1000 -0.0006
620 0.6941 nan 0.1000 -0.0010
640 0.6924 nan 0.1000 -0.0005
660 0.6903 nan 0.1000 -0.0006
680 0.6888 nan 0.1000 -0.0011
700 0.6871 nan 0.1000 -0.0017
720 0.6852 nan 0.1000 -0.0008
740 0.6839 nan 0.1000 -0.0006
760 0.6821 nan 0.1000 -0.0003
780 0.6809 nan 0.1000 -0.0007
800 0.6794 nan 0.1000 -0.0010
820 0.6766 nan 0.1000 -0.0008
840 0.6739 nan 0.1000 -0.0004
860 0.6722 nan 0.1000 -0.0007
880 0.6712 nan 0.1000 -0.0007
900 0.6692 nan 0.1000 -0.0008
920 0.6669 nan 0.1000 -0.0003
940 0.6648 nan 0.1000 -0.0012
960 0.6627 nan 0.1000 -0.0001
980 0.6625 nan 0.1000 -0.0004
1000 0.6605 nan 0.1000 -0.0007
1020 0.6588 nan 0.1000 -0.0006
1040 0.6575 nan 0.1000 -0.0003
1060 0.6561 nan 0.1000 -0.0004
1080 0.6544 nan 0.1000 -0.0007
1100 0.6532 nan 0.1000 -0.0009
- Fold02.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2643 nan 0.1000 0.0350
2 1.2107 nan 0.1000 0.0256
3 1.1611 nan 0.1000 0.0255
4 1.1210 nan 0.1000 0.0210
5 1.0843 nan 0.1000 0.0174
6 1.0591 nan 0.1000 0.0143
7 1.0334 nan 0.1000 0.0135
8 1.0079 nan 0.1000 0.0116
9 0.9875 nan 0.1000 0.0082
10 0.9683 nan 0.1000 0.0086
20 0.8712 nan 0.1000 0.0012
40 0.7998 nan 0.1000 -0.0003
60 0.7641 nan 0.1000 -0.0006
80 0.7342 nan 0.1000 -0.0005
100 0.7149 nan 0.1000 -0.0010
120 0.6972 nan 0.1000 -0.0014
140 0.6811 nan 0.1000 -0.0016
160 0.6612 nan 0.1000 -0.0006
180 0.6499 nan 0.1000 -0.0014
200 0.6347 nan 0.1000 -0.0009
220 0.6233 nan 0.1000 -0.0014
240 0.6136 nan 0.1000 -0.0009
260 0.6009 nan 0.1000 -0.0007
280 0.5912 nan 0.1000 -0.0011
300 0.5773 nan 0.1000 -0.0004
320 0.5713 nan 0.1000 -0.0007
340 0.5634 nan 0.1000 -0.0008
360 0.5558 nan 0.1000 -0.0003
380 0.5489 nan 0.1000 -0.0007
400 0.5442 nan 0.1000 -0.0010
420 0.5380 nan 0.1000 -0.0010
440 0.5319 nan 0.1000 -0.0007
460 0.5265 nan 0.1000 -0.0012
480 0.5213 nan 0.1000 -0.0008
500 0.5143 nan 0.1000 -0.0006
520 0.5093 nan 0.1000 -0.0011
540 0.5044 nan 0.1000 -0.0003
560 0.5004 nan 0.1000 -0.0008
580 0.4932 nan 0.1000 -0.0014
600 0.4873 nan 0.1000 -0.0006
620 0.4812 nan 0.1000 -0.0006
640 0.4782 nan 0.1000 -0.0012
660 0.4732 nan 0.1000 -0.0016
680 0.4697 nan 0.1000 -0.0009
700 0.4630 nan 0.1000 -0.0009
720 0.4584 nan 0.1000 -0.0011
740 0.4546 nan 0.1000 -0.0008
760 0.4514 nan 0.1000 -0.0005
780 0.4472 nan 0.1000 -0.0012
800 0.4440 nan 0.1000 -0.0008
820 0.4405 nan 0.1000 -0.0012
840 0.4370 nan 0.1000 -0.0011
860 0.4313 nan 0.1000 -0.0009
880 0.4277 nan 0.1000 -0.0005
900 0.4242 nan 0.1000 -0.0007
920 0.4208 nan 0.1000 -0.0005
940 0.4177 nan 0.1000 -0.0006
960 0.4154 nan 0.1000 -0.0009
980 0.4124 nan 0.1000 -0.0007
1000 0.4086 nan 0.1000 -0.0009
1020 0.4035 nan 0.1000 -0.0010
1040 0.4001 nan 0.1000 -0.0006
1060 0.3968 nan 0.1000 -0.0013
1080 0.3931 nan 0.1000 -0.0009
1100 0.3915 nan 0.1000 -0.0006
- Fold02.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2578 nan 0.1000 0.0362
2 1.1957 nan 0.1000 0.0305
3 1.1439 nan 0.1000 0.0217
4 1.1012 nan 0.1000 0.0212
5 1.0573 nan 0.1000 0.0193
6 1.0265 nan 0.1000 0.0161
7 0.9970 nan 0.1000 0.0126
8 0.9700 nan 0.1000 0.0104
9 0.9524 nan 0.1000 0.0082
10 0.9347 nan 0.1000 0.0074
20 0.8324 nan 0.1000 -0.0008
40 0.7543 nan 0.1000 0.0002
60 0.7187 nan 0.1000 -0.0009
80 0.6847 nan 0.1000 -0.0009
100 0.6594 nan 0.1000 -0.0007
120 0.6356 nan 0.1000 -0.0026
140 0.6129 nan 0.1000 -0.0002
160 0.5942 nan 0.1000 -0.0023
180 0.5709 nan 0.1000 -0.0007
200 0.5550 nan 0.1000 -0.0012
220 0.5362 nan 0.1000 -0.0010
240 0.5244 nan 0.1000 -0.0007
260 0.5101 nan 0.1000 -0.0000
280 0.5006 nan 0.1000 -0.0026
300 0.4895 nan 0.1000 -0.0008
320 0.4774 nan 0.1000 -0.0012
340 0.4667 nan 0.1000 -0.0011
360 0.4561 nan 0.1000 -0.0008
380 0.4444 nan 0.1000 -0.0010
400 0.4368 nan 0.1000 -0.0013
420 0.4293 nan 0.1000 -0.0004
440 0.4219 nan 0.1000 -0.0005
460 0.4132 nan 0.1000 -0.0011
480 0.4078 nan 0.1000 -0.0007
500 0.3999 nan 0.1000 -0.0012
520 0.3918 nan 0.1000 -0.0009
540 0.3848 nan 0.1000 -0.0007
560 0.3806 nan 0.1000 -0.0012
580 0.3738 nan 0.1000 -0.0004
600 0.3673 nan 0.1000 -0.0013
620 0.3600 nan 0.1000 -0.0010
640 0.3540 nan 0.1000 -0.0013
660 0.3473 nan 0.1000 -0.0008
680 0.3421 nan 0.1000 -0.0005
700 0.3370 nan 0.1000 -0.0005
720 0.3298 nan 0.1000 -0.0015
740 0.3237 nan 0.1000 -0.0014
760 0.3209 nan 0.1000 -0.0009
780 0.3156 nan 0.1000 -0.0012
800 0.3109 nan 0.1000 -0.0007
820 0.3069 nan 0.1000 -0.0009
840 0.3024 nan 0.1000 -0.0012
860 0.2980 nan 0.1000 -0.0010
880 0.2937 nan 0.1000 -0.0008
900 0.2897 nan 0.1000 -0.0008
920 0.2858 nan 0.1000 -0.0010
940 0.2825 nan 0.1000 -0.0006
960 0.2799 nan 0.1000 -0.0009
980 0.2755 nan 0.1000 -0.0011
1000 0.2707 nan 0.1000 -0.0006
1020 0.2663 nan 0.1000 -0.0011
1040 0.2636 nan 0.1000 -0.0008
1060 0.2605 nan 0.1000 -0.0007
1080 0.2573 nan 0.1000 -0.0009
1100 0.2542 nan 0.1000 -0.0006
- Fold02.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3253 nan 0.0100 0.0031
2 1.3194 nan 0.0100 0.0029
3 1.3132 nan 0.0100 0.0030
4 1.3074 nan 0.0100 0.0028
5 1.3013 nan 0.0100 0.0029
6 1.2958 nan 0.0100 0.0026
7 1.2905 nan 0.0100 0.0028
8 1.2852 nan 0.0100 0.0026
9 1.2796 nan 0.0100 0.0027
10 1.2745 nan 0.0100 0.0025
20 1.2282 nan 0.0100 0.0022
40 1.1568 nan 0.0100 0.0014
60 1.1055 nan 0.0100 0.0011
80 1.0700 nan 0.0100 0.0008
100 1.0385 nan 0.0100 0.0006
120 1.0131 nan 0.0100 0.0005
140 0.9926 nan 0.0100 0.0003
160 0.9746 nan 0.0100 0.0004
180 0.9590 nan 0.0100 0.0002
200 0.9448 nan 0.0100 0.0003
220 0.9319 nan 0.0100 0.0001
240 0.9205 nan 0.0100 0.0002
260 0.9110 nan 0.0100 0.0001
280 0.9021 nan 0.0100 0.0001
300 0.8933 nan 0.0100 0.0001
320 0.8858 nan 0.0100 -0.0000
340 0.8789 nan 0.0100 0.0001
360 0.8732 nan 0.0100 0.0001
380 0.8670 nan 0.0100 0.0001
400 0.8617 nan 0.0100 0.0000
420 0.8569 nan 0.0100 0.0000
440 0.8523 nan 0.0100 0.0001
460 0.8478 nan 0.0100 -0.0000
480 0.8435 nan 0.0100 -0.0000
500 0.8395 nan 0.0100 0.0001
520 0.8355 nan 0.0100 0.0000
540 0.8317 nan 0.0100 0.0000
560 0.8284 nan 0.0100 -0.0000
580 0.8252 nan 0.0100 0.0000
600 0.8219 nan 0.0100 0.0000
620 0.8188 nan 0.0100 0.0000
640 0.8162 nan 0.0100 -0.0000
660 0.8134 nan 0.0100 0.0000
680 0.8109 nan 0.0100 -0.0000
700 0.8083 nan 0.0100 0.0000
720 0.8057 nan 0.0100 0.0000
740 0.8034 nan 0.0100 0.0000
760 0.8013 nan 0.0100 0.0000
780 0.7991 nan 0.0100 -0.0000
800 0.7972 nan 0.0100 -0.0000
820 0.7953 nan 0.0100 -0.0000
840 0.7933 nan 0.0100 -0.0000
860 0.7918 nan 0.0100 -0.0001
880 0.7900 nan 0.0100 -0.0000
900 0.7883 nan 0.0100 -0.0002
920 0.7865 nan 0.0100 -0.0000
940 0.7850 nan 0.0100 -0.0000
960 0.7832 nan 0.0100 0.0000
980 0.7816 nan 0.0100 0.0000
1000 0.7800 nan 0.0100 -0.0001
1020 0.7785 nan 0.0100 -0.0000
1040 0.7771 nan 0.0100 -0.0001
1060 0.7758 nan 0.0100 -0.0001
1080 0.7744 nan 0.0100 -0.0000
1100 0.7730 nan 0.0100 -0.0001
- Fold03.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3240 nan 0.0100 0.0037
2 1.3164 nan 0.0100 0.0035
3 1.3085 nan 0.0100 0.0036
4 1.3011 nan 0.0100 0.0037
5 1.2935 nan 0.0100 0.0037
6 1.2864 nan 0.0100 0.0034
7 1.2791 nan 0.0100 0.0034
8 1.2717 nan 0.0100 0.0033
9 1.2651 nan 0.0100 0.0032
10 1.2584 nan 0.0100 0.0030
20 1.2005 nan 0.0100 0.0027
40 1.1104 nan 0.0100 0.0019
60 1.0445 nan 0.0100 0.0013
80 0.9942 nan 0.0100 0.0010
100 0.9572 nan 0.0100 0.0006
120 0.9270 nan 0.0100 0.0006
140 0.9035 nan 0.0100 0.0004
160 0.8843 nan 0.0100 0.0004
180 0.8687 nan 0.0100 0.0002
200 0.8551 nan 0.0100 0.0002
220 0.8430 nan 0.0100 0.0002
240 0.8326 nan 0.0100 0.0000
260 0.8232 nan 0.0100 0.0000
280 0.8147 nan 0.0100 0.0001
300 0.8077 nan 0.0100 0.0000
320 0.8005 nan 0.0100 -0.0000
340 0.7939 nan 0.0100 0.0000
360 0.7886 nan 0.0100 -0.0000
380 0.7831 nan 0.0100 0.0000
400 0.7776 nan 0.0100 0.0000
420 0.7731 nan 0.0100 0.0000
440 0.7689 nan 0.0100 -0.0000
460 0.7642 nan 0.0100 -0.0001
480 0.7598 nan 0.0100 -0.0000
500 0.7563 nan 0.0100 -0.0001
520 0.7531 nan 0.0100 -0.0000
540 0.7497 nan 0.0100 -0.0000
560 0.7464 nan 0.0100 -0.0001
580 0.7434 nan 0.0100 -0.0001
600 0.7402 nan 0.0100 -0.0001
620 0.7374 nan 0.0100 0.0001
640 0.7344 nan 0.0100 -0.0000
660 0.7317 nan 0.0100 -0.0001
680 0.7285 nan 0.0100 -0.0003
700 0.7257 nan 0.0100 -0.0001
720 0.7232 nan 0.0100 -0.0001
740 0.7208 nan 0.0100 -0.0001
760 0.7183 nan 0.0100 -0.0000
780 0.7162 nan 0.0100 -0.0000
800 0.7140 nan 0.0100 -0.0000
820 0.7119 nan 0.0100 -0.0001
840 0.7099 nan 0.0100 -0.0001
860 0.7074 nan 0.0100 -0.0001
880 0.7054 nan 0.0100 -0.0001
900 0.7035 nan 0.0100 -0.0001
920 0.7011 nan 0.0100 -0.0000
940 0.6992 nan 0.0100 -0.0001
960 0.6973 nan 0.0100 -0.0000
980 0.6956 nan 0.0100 -0.0002
1000 0.6938 nan 0.0100 -0.0001
1020 0.6919 nan 0.0100 -0.0001
1040 0.6899 nan 0.0100 -0.0001
1060 0.6882 nan 0.0100 -0.0001
1080 0.6862 nan 0.0100 -0.0001
1100 0.6841 nan 0.0100 -0.0001
- Fold03.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3230 nan 0.0100 0.0042
2 1.3142 nan 0.0100 0.0039
3 1.3054 nan 0.0100 0.0039
4 1.2976 nan 0.0100 0.0037
5 1.2899 nan 0.0100 0.0038
6 1.2820 nan 0.0100 0.0039
7 1.2738 nan 0.0100 0.0039
8 1.2667 nan 0.0100 0.0039
9 1.2587 nan 0.0100 0.0039
10 1.2514 nan 0.0100 0.0036
20 1.1884 nan 0.0100 0.0026
40 1.0885 nan 0.0100 0.0017
60 1.0170 nan 0.0100 0.0014
80 0.9632 nan 0.0100 0.0011
100 0.9211 nan 0.0100 0.0008
120 0.8899 nan 0.0100 0.0005
140 0.8665 nan 0.0100 0.0002
160 0.8465 nan 0.0100 0.0004
180 0.8302 nan 0.0100 0.0003
200 0.8159 nan 0.0100 0.0002
220 0.8033 nan 0.0100 0.0001
240 0.7923 nan 0.0100 0.0001
260 0.7829 nan 0.0100 0.0000
280 0.7747 nan 0.0100 -0.0000
300 0.7671 nan 0.0100 -0.0001
320 0.7611 nan 0.0100 -0.0000
340 0.7536 nan 0.0100 -0.0000
360 0.7469 nan 0.0100 -0.0000
380 0.7412 nan 0.0100 -0.0000
400 0.7357 nan 0.0100 -0.0002
420 0.7301 nan 0.0100 0.0000
440 0.7256 nan 0.0100 -0.0000
460 0.7207 nan 0.0100 -0.0001
480 0.7163 nan 0.0100 0.0001
500 0.7118 nan 0.0100 -0.0001
520 0.7080 nan 0.0100 -0.0001
540 0.7043 nan 0.0100 -0.0000
560 0.7002 nan 0.0100 -0.0001
580 0.6961 nan 0.0100 -0.0001
600 0.6924 nan 0.0100 -0.0001
620 0.6883 nan 0.0100 -0.0000
640 0.6851 nan 0.0100 -0.0001
660 0.6817 nan 0.0100 -0.0002
680 0.6786 nan 0.0100 -0.0001
700 0.6752 nan 0.0100 -0.0001
720 0.6720 nan 0.0100 -0.0002
740 0.6688 nan 0.0100 -0.0001
760 0.6659 nan 0.0100 -0.0001
780 0.6631 nan 0.0100 -0.0001
800 0.6603 nan 0.0100 -0.0001
820 0.6576 nan 0.0100 -0.0001
840 0.6545 nan 0.0100 -0.0001
860 0.6517 nan 0.0100 0.0000
880 0.6489 nan 0.0100 -0.0001
900 0.6463 nan 0.0100 -0.0001
920 0.6437 nan 0.0100 -0.0001
940 0.6412 nan 0.0100 -0.0000
960 0.6387 nan 0.0100 -0.0001
980 0.6365 nan 0.0100 -0.0002
1000 0.6340 nan 0.0100 -0.0001
1020 0.6315 nan 0.0100 -0.0000
1040 0.6288 nan 0.0100 -0.0001
1060 0.6265 nan 0.0100 -0.0002
1080 0.6242 nan 0.0100 -0.0001
1100 0.6218 nan 0.0100 -0.0001
- Fold03.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2717 nan 0.1000 0.0297
2 1.2242 nan 0.1000 0.0244
3 1.1826 nan 0.1000 0.0196
4 1.1508 nan 0.1000 0.0156
5 1.1276 nan 0.1000 0.0129
6 1.1030 nan 0.1000 0.0115
7 1.0881 nan 0.1000 0.0065
8 1.0667 nan 0.1000 0.0083
9 1.0509 nan 0.1000 0.0068
10 1.0337 nan 0.1000 0.0069
20 0.9410 nan 0.1000 0.0021
40 0.8618 nan 0.1000 0.0006
60 0.8202 nan 0.1000 -0.0002
80 0.7962 nan 0.1000 -0.0002
100 0.7810 nan 0.1000 0.0002
120 0.7689 nan 0.1000 -0.0005
140 0.7611 nan 0.1000 -0.0015
160 0.7502 nan 0.1000 0.0000
180 0.7426 nan 0.1000 -0.0004
200 0.7356 nan 0.1000 -0.0007
220 0.7287 nan 0.1000 -0.0005
240 0.7222 nan 0.1000 -0.0004
260 0.7184 nan 0.1000 -0.0004
280 0.7149 nan 0.1000 -0.0007
300 0.7108 nan 0.1000 -0.0006
320 0.7079 nan 0.1000 -0.0008
340 0.7041 nan 0.1000 -0.0011
360 0.7009 nan 0.1000 -0.0012
380 0.6958 nan 0.1000 -0.0002
400 0.6930 nan 0.1000 -0.0005
420 0.6913 nan 0.1000 -0.0010
440 0.6880 nan 0.1000 -0.0006
460 0.6851 nan 0.1000 -0.0003
480 0.6824 nan 0.1000 -0.0007
500 0.6804 nan 0.1000 -0.0007
520 0.6782 nan 0.1000 -0.0016
540 0.6765 nan 0.1000 -0.0005
560 0.6739 nan 0.1000 -0.0006
580 0.6725 nan 0.1000 -0.0008
600 0.6700 nan 0.1000 -0.0013
620 0.6679 nan 0.1000 -0.0005
640 0.6670 nan 0.1000 -0.0008
660 0.6650 nan 0.1000 -0.0008
680 0.6628 nan 0.1000 -0.0006
700 0.6603 nan 0.1000 -0.0003
720 0.6599 nan 0.1000 -0.0013
740 0.6579 nan 0.1000 -0.0012
760 0.6556 nan 0.1000 -0.0007
780 0.6534 nan 0.1000 -0.0006
800 0.6527 nan 0.1000 -0.0015
820 0.6515 nan 0.1000 -0.0014
840 0.6499 nan 0.1000 -0.0008
860 0.6475 nan 0.1000 -0.0006
880 0.6450 nan 0.1000 -0.0008
900 0.6443 nan 0.1000 -0.0005
920 0.6431 nan 0.1000 -0.0006
940 0.6420 nan 0.1000 -0.0004
960 0.6407 nan 0.1000 -0.0008
980 0.6388 nan 0.1000 -0.0009
1000 0.6378 nan 0.1000 -0.0006
1020 0.6359 nan 0.1000 -0.0007
1040 0.6349 nan 0.1000 -0.0007
1060 0.6345 nan 0.1000 -0.0013
1080 0.6329 nan 0.1000 -0.0003
1100 0.6316 nan 0.1000 -0.0009
- Fold03.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2574 nan 0.1000 0.0355
2 1.1965 nan 0.1000 0.0282
3 1.1465 nan 0.1000 0.0263
4 1.1040 nan 0.1000 0.0204
5 1.0692 nan 0.1000 0.0179
6 1.0368 nan 0.1000 0.0154
7 1.0090 nan 0.1000 0.0124
8 0.9849 nan 0.1000 0.0102
9 0.9628 nan 0.1000 0.0092
10 0.9447 nan 0.1000 0.0073
20 0.8535 nan 0.1000 0.0021
40 0.7753 nan 0.1000 0.0005
60 0.7368 nan 0.1000 -0.0010
80 0.7155 nan 0.1000 -0.0008
100 0.6990 nan 0.1000 -0.0007
120 0.6818 nan 0.1000 -0.0012
140 0.6610 nan 0.1000 0.0001
160 0.6468 nan 0.1000 -0.0019
180 0.6346 nan 0.1000 -0.0013
200 0.6205 nan 0.1000 -0.0006
220 0.6092 nan 0.1000 -0.0009
240 0.6024 nan 0.1000 -0.0012
260 0.5926 nan 0.1000 -0.0007
280 0.5842 nan 0.1000 -0.0011
300 0.5752 nan 0.1000 -0.0011
320 0.5678 nan 0.1000 -0.0013
340 0.5588 nan 0.1000 -0.0009
360 0.5522 nan 0.1000 -0.0003
380 0.5462 nan 0.1000 -0.0010
400 0.5395 nan 0.1000 -0.0013
420 0.5318 nan 0.1000 -0.0006
440 0.5264 nan 0.1000 -0.0011
460 0.5191 nan 0.1000 -0.0008
480 0.5111 nan 0.1000 -0.0007
500 0.5053 nan 0.1000 -0.0014
520 0.5005 nan 0.1000 -0.0007
540 0.4947 nan 0.1000 -0.0005
560 0.4905 nan 0.1000 -0.0013
580 0.4841 nan 0.1000 -0.0009
600 0.4792 nan 0.1000 -0.0011
620 0.4739 nan 0.1000 -0.0005
640 0.4682 nan 0.1000 -0.0014
660 0.4647 nan 0.1000 -0.0008
680 0.4601 nan 0.1000 -0.0013
700 0.4558 nan 0.1000 -0.0014
720 0.4516 nan 0.1000 -0.0013
740 0.4466 nan 0.1000 -0.0007
760 0.4425 nan 0.1000 -0.0014
780 0.4398 nan 0.1000 -0.0009
800 0.4357 nan 0.1000 -0.0009
820 0.4308 nan 0.1000 -0.0017
840 0.4261 nan 0.1000 -0.0010
860 0.4240 nan 0.1000 -0.0007
880 0.4202 nan 0.1000 -0.0012
900 0.4180 nan 0.1000 -0.0006
920 0.4136 nan 0.1000 -0.0006
940 0.4121 nan 0.1000 -0.0006
960 0.4086 nan 0.1000 -0.0014
980 0.4053 nan 0.1000 -0.0009
1000 0.4029 nan 0.1000 -0.0010
1020 0.3999 nan 0.1000 -0.0018
1040 0.3973 nan 0.1000 -0.0005
1060 0.3922 nan 0.1000 -0.0005
1080 0.3895 nan 0.1000 -0.0014
1100 0.3872 nan 0.1000 -0.0008
- Fold03.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2546 nan 0.1000 0.0402
2 1.1934 nan 0.1000 0.0284
3 1.1365 nan 0.1000 0.0263
4 1.0862 nan 0.1000 0.0226
5 1.0437 nan 0.1000 0.0193
6 1.0092 nan 0.1000 0.0154
7 0.9816 nan 0.1000 0.0138
8 0.9585 nan 0.1000 0.0097
9 0.9351 nan 0.1000 0.0091
10 0.9175 nan 0.1000 0.0075
20 0.8178 nan 0.1000 0.0027
40 0.7390 nan 0.1000 -0.0011
60 0.6981 nan 0.1000 -0.0005
80 0.6666 nan 0.1000 -0.0008
100 0.6413 nan 0.1000 -0.0025
120 0.6158 nan 0.1000 -0.0009
140 0.5959 nan 0.1000 -0.0010
160 0.5782 nan 0.1000 -0.0010
180 0.5616 nan 0.1000 -0.0013
200 0.5458 nan 0.1000 -0.0008
220 0.5324 nan 0.1000 -0.0024
240 0.5156 nan 0.1000 -0.0008
260 0.5054 nan 0.1000 -0.0014
280 0.4933 nan 0.1000 -0.0018
300 0.4804 nan 0.1000 -0.0008
320 0.4692 nan 0.1000 -0.0012
340 0.4570 nan 0.1000 -0.0009
360 0.4494 nan 0.1000 -0.0011
380 0.4427 nan 0.1000 -0.0014
400 0.4324 nan 0.1000 -0.0001
420 0.4245 nan 0.1000 -0.0010
440 0.4152 nan 0.1000 -0.0012
460 0.4079 nan 0.1000 -0.0011
480 0.3999 nan 0.1000 -0.0006
500 0.3919 nan 0.1000 -0.0013
520 0.3869 nan 0.1000 -0.0008
540 0.3801 nan 0.1000 -0.0008
560 0.3746 nan 0.1000 -0.0008
580 0.3677 nan 0.1000 -0.0007
600 0.3618 nan 0.1000 -0.0017
620 0.3553 nan 0.1000 -0.0005
640 0.3497 nan 0.1000 -0.0014
660 0.3435 nan 0.1000 -0.0007
680 0.3395 nan 0.1000 -0.0012
700 0.3350 nan 0.1000 -0.0006
720 0.3307 nan 0.1000 -0.0011
740 0.3263 nan 0.1000 -0.0007
760 0.3234 nan 0.1000 -0.0014
780 0.3191 nan 0.1000 -0.0011
800 0.3143 nan 0.1000 -0.0009
820 0.3119 nan 0.1000 -0.0008
840 0.3061 nan 0.1000 -0.0005
860 0.3020 nan 0.1000 -0.0010
880 0.2972 nan 0.1000 -0.0007
900 0.2921 nan 0.1000 -0.0007
920 0.2894 nan 0.1000 -0.0011
940 0.2858 nan 0.1000 -0.0005
960 0.2823 nan 0.1000 -0.0005
980 0.2787 nan 0.1000 -0.0007
1000 0.2749 nan 0.1000 -0.0005
1020 0.2710 nan 0.1000 -0.0005
1040 0.2681 nan 0.1000 -0.0010
1060 0.2644 nan 0.1000 -0.0006
1080 0.2624 nan 0.1000 -0.0010
1100 0.2594 nan 0.1000 -0.0008
- Fold03.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3252 nan 0.0100 0.0030
2 1.3191 nan 0.0100 0.0029
3 1.3135 nan 0.0100 0.0027
4 1.3081 nan 0.0100 0.0027
5 1.3026 nan 0.0100 0.0027
6 1.2975 nan 0.0100 0.0027
7 1.2926 nan 0.0100 0.0025
8 1.2872 nan 0.0100 0.0026
9 1.2819 nan 0.0100 0.0025
10 1.2772 nan 0.0100 0.0025
20 1.2336 nan 0.0100 0.0020
40 1.1656 nan 0.0100 0.0014
60 1.1184 nan 0.0100 0.0010
80 1.0791 nan 0.0100 0.0008
100 1.0480 nan 0.0100 0.0006
120 1.0222 nan 0.0100 0.0005
140 1.0010 nan 0.0100 0.0005
160 0.9816 nan 0.0100 0.0004
180 0.9657 nan 0.0100 0.0003
200 0.9521 nan 0.0100 0.0003
220 0.9391 nan 0.0100 0.0002
240 0.9283 nan 0.0100 0.0002
260 0.9191 nan 0.0100 0.0001
280 0.9100 nan 0.0100 0.0002
300 0.9020 nan 0.0100 0.0001
320 0.8947 nan 0.0100 0.0001
340 0.8877 nan 0.0100 0.0001
360 0.8811 nan 0.0100 0.0001
380 0.8755 nan 0.0100 0.0000
400 0.8701 nan 0.0100 0.0001
420 0.8652 nan 0.0100 0.0001
440 0.8603 nan 0.0100 0.0001
460 0.8558 nan 0.0100 0.0000
480 0.8511 nan 0.0100 0.0000
500 0.8471 nan 0.0100 0.0001
520 0.8432 nan 0.0100 0.0000
540 0.8393 nan 0.0100 -0.0000
560 0.8358 nan 0.0100 0.0000
580 0.8321 nan 0.0100 0.0000
600 0.8289 nan 0.0100 0.0000
620 0.8258 nan 0.0100 -0.0000
640 0.8231 nan 0.0100 -0.0000
660 0.8202 nan 0.0100 -0.0000
680 0.8178 nan 0.0100 0.0001
700 0.8148 nan 0.0100 0.0000
720 0.8123 nan 0.0100 0.0000
740 0.8098 nan 0.0100 -0.0000
760 0.8073 nan 0.0100 0.0000
780 0.8049 nan 0.0100 0.0000
800 0.8024 nan 0.0100 -0.0000
820 0.8002 nan 0.0100 -0.0000
840 0.7983 nan 0.0100 -0.0000
860 0.7961 nan 0.0100 -0.0000
880 0.7939 nan 0.0100 -0.0000
900 0.7923 nan 0.0100 -0.0000
920 0.7907 nan 0.0100 -0.0001
940 0.7890 nan 0.0100 -0.0001
960 0.7871 nan 0.0100 -0.0000
980 0.7854 nan 0.0100 -0.0000
1000 0.7836 nan 0.0100 0.0000
1020 0.7821 nan 0.0100 0.0000
1040 0.7806 nan 0.0100 -0.0000
1060 0.7789 nan 0.0100 -0.0000
1080 0.7776 nan 0.0100 -0.0001
1100 0.7763 nan 0.0100 -0.0000
- Fold04.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3240 nan 0.0100 0.0036
2 1.3165 nan 0.0100 0.0036
3 1.3091 nan 0.0100 0.0035
4 1.3022 nan 0.0100 0.0033
5 1.2954 nan 0.0100 0.0029
6 1.2891 nan 0.0100 0.0033
7 1.2824 nan 0.0100 0.0032
8 1.2761 nan 0.0100 0.0032
9 1.2698 nan 0.0100 0.0033
10 1.2635 nan 0.0100 0.0030
20 1.2070 nan 0.0100 0.0025
40 1.1186 nan 0.0100 0.0018
60 1.0550 nan 0.0100 0.0013
80 1.0073 nan 0.0100 0.0006
100 0.9693 nan 0.0100 0.0008
120 0.9409 nan 0.0100 0.0007
140 0.9165 nan 0.0100 0.0005
160 0.8982 nan 0.0100 0.0003
180 0.8824 nan 0.0100 0.0004
200 0.8691 nan 0.0100 0.0002
220 0.8557 nan 0.0100 0.0002
240 0.8446 nan 0.0100 0.0002
260 0.8349 nan 0.0100 0.0001
280 0.8258 nan 0.0100 0.0002
300 0.8178 nan 0.0100 -0.0000
320 0.8100 nan 0.0100 0.0001
340 0.8037 nan 0.0100 0.0000
360 0.7971 nan 0.0100 0.0000
380 0.7913 nan 0.0100 0.0000
400 0.7859 nan 0.0100 -0.0001
420 0.7806 nan 0.0100 0.0000
440 0.7759 nan 0.0100 0.0000
460 0.7711 nan 0.0100 -0.0000
480 0.7672 nan 0.0100 -0.0001
500 0.7633 nan 0.0100 0.0000
520 0.7596 nan 0.0100 -0.0001
540 0.7558 nan 0.0100 -0.0001
560 0.7517 nan 0.0100 -0.0000
580 0.7487 nan 0.0100 -0.0001
600 0.7458 nan 0.0100 0.0000
620 0.7427 nan 0.0100 0.0000
640 0.7396 nan 0.0100 -0.0001
660 0.7369 nan 0.0100 -0.0000
680 0.7346 nan 0.0100 -0.0001
700 0.7316 nan 0.0100 -0.0000
720 0.7288 nan 0.0100 -0.0001
740 0.7263 nan 0.0100 -0.0000
760 0.7236 nan 0.0100 -0.0000
780 0.7209 nan 0.0100 -0.0001
800 0.7185 nan 0.0100 -0.0001
820 0.7158 nan 0.0100 -0.0001
840 0.7133 nan 0.0100 -0.0001
860 0.7115 nan 0.0100 -0.0001
880 0.7094 nan 0.0100 -0.0001
900 0.7071 nan 0.0100 -0.0002
920 0.7050 nan 0.0100 -0.0000
940 0.7028 nan 0.0100 -0.0001
960 0.7007 nan 0.0100 -0.0000
980 0.6988 nan 0.0100 -0.0001
1000 0.6967 nan 0.0100 -0.0001
1020 0.6950 nan 0.0100 -0.0001
1040 0.6933 nan 0.0100 -0.0001
1060 0.6915 nan 0.0100 -0.0000
1080 0.6895 nan 0.0100 -0.0001
1100 0.6877 nan 0.0100 -0.0001
- Fold04.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3230 nan 0.0100 0.0040
2 1.3152 nan 0.0100 0.0040
3 1.3068 nan 0.0100 0.0040
4 1.2990 nan 0.0100 0.0037
5 1.2916 nan 0.0100 0.0037
6 1.2835 nan 0.0100 0.0035
7 1.2755 nan 0.0100 0.0036
8 1.2686 nan 0.0100 0.0036
9 1.2611 nan 0.0100 0.0033
10 1.2539 nan 0.0100 0.0032
20 1.1919 nan 0.0100 0.0027
40 1.0963 nan 0.0100 0.0014
60 1.0270 nan 0.0100 0.0012
80 0.9752 nan 0.0100 0.0008
100 0.9341 nan 0.0100 0.0008
120 0.9022 nan 0.0100 0.0006
140 0.8774 nan 0.0100 0.0004
160 0.8575 nan 0.0100 0.0003
180 0.8411 nan 0.0100 0.0001
200 0.8263 nan 0.0100 0.0002
220 0.8138 nan 0.0100 -0.0000
240 0.8029 nan 0.0100 0.0000
260 0.7938 nan 0.0100 -0.0001
280 0.7853 nan 0.0100 -0.0000
300 0.7772 nan 0.0100 0.0000
320 0.7696 nan 0.0100 -0.0001
340 0.7624 nan 0.0100 0.0001
360 0.7557 nan 0.0100 -0.0000
380 0.7496 nan 0.0100 -0.0001
400 0.7438 nan 0.0100 -0.0001
420 0.7383 nan 0.0100 -0.0002
440 0.7325 nan 0.0100 -0.0001
460 0.7275 nan 0.0100 -0.0000
480 0.7229 nan 0.0100 -0.0001
500 0.7185 nan 0.0100 -0.0001
520 0.7140 nan 0.0100 -0.0000
540 0.7094 nan 0.0100 0.0000
560 0.7051 nan 0.0100 0.0000
580 0.7014 nan 0.0100 -0.0002
600 0.6978 nan 0.0100 -0.0001
620 0.6933 nan 0.0100 -0.0002
640 0.6897 nan 0.0100 -0.0001
660 0.6858 nan 0.0100 0.0000
680 0.6823 nan 0.0100 -0.0002
700 0.6785 nan 0.0100 0.0000
720 0.6749 nan 0.0100 -0.0000
740 0.6715 nan 0.0100 -0.0002
760 0.6686 nan 0.0100 -0.0001
780 0.6659 nan 0.0100 -0.0000
800 0.6634 nan 0.0100 -0.0002
820 0.6605 nan 0.0100 0.0000
840 0.6577 nan 0.0100 -0.0002
860 0.6546 nan 0.0100 -0.0000
880 0.6519 nan 0.0100 -0.0002
900 0.6492 nan 0.0100 -0.0001
920 0.6462 nan 0.0100 -0.0001
940 0.6435 nan 0.0100 -0.0001
960 0.6412 nan 0.0100 -0.0001
980 0.6384 nan 0.0100 -0.0001
1000 0.6360 nan 0.0100 -0.0002
1020 0.6337 nan 0.0100 -0.0001
1040 0.6312 nan 0.0100 -0.0001
1060 0.6289 nan 0.0100 -0.0001
1080 0.6265 nan 0.0100 -0.0000
1100 0.6243 nan 0.0100 -0.0001
- Fold04.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2702 nan 0.1000 0.0267
2 1.2260 nan 0.1000 0.0229
3 1.1902 nan 0.1000 0.0173
4 1.1593 nan 0.1000 0.0157
5 1.1323 nan 0.1000 0.0131
6 1.1120 nan 0.1000 0.0085
7 1.0902 nan 0.1000 0.0102
8 1.0721 nan 0.1000 0.0084
9 1.0566 nan 0.1000 0.0073
10 1.0412 nan 0.1000 0.0071
20 0.9502 nan 0.1000 0.0020
40 0.8674 nan 0.1000 0.0008
60 0.8309 nan 0.1000 -0.0002
80 0.8039 nan 0.1000 -0.0003
100 0.7842 nan 0.1000 -0.0003
120 0.7679 nan 0.1000 -0.0007
140 0.7584 nan 0.1000 -0.0008
160 0.7488 nan 0.1000 -0.0003
180 0.7414 nan 0.1000 -0.0006
200 0.7343 nan 0.1000 -0.0004
220 0.7279 nan 0.1000 -0.0001
240 0.7215 nan 0.1000 -0.0009
260 0.7172 nan 0.1000 -0.0003
280 0.7117 nan 0.1000 -0.0005
300 0.7083 nan 0.1000 -0.0006
320 0.7030 nan 0.1000 -0.0012
340 0.6988 nan 0.1000 -0.0005
360 0.6952 nan 0.1000 -0.0005
380 0.6927 nan 0.1000 -0.0010
400 0.6893 nan 0.1000 -0.0008
420 0.6865 nan 0.1000 -0.0007
440 0.6830 nan 0.1000 -0.0013
460 0.6810 nan 0.1000 -0.0009
480 0.6782 nan 0.1000 -0.0013
500 0.6765 nan 0.1000 -0.0006
520 0.6742 nan 0.1000 -0.0007
540 0.6728 nan 0.1000 -0.0005
560 0.6708 nan 0.1000 -0.0012
580 0.6688 nan 0.1000 -0.0012
600 0.6672 nan 0.1000 -0.0009
620 0.6655 nan 0.1000 -0.0005
640 0.6640 nan 0.1000 -0.0006
660 0.6618 nan 0.1000 -0.0006
680 0.6604 nan 0.1000 -0.0006
700 0.6582 nan 0.1000 -0.0006
720 0.6564 nan 0.1000 -0.0005
740 0.6544 nan 0.1000 -0.0013
760 0.6527 nan 0.1000 -0.0009
780 0.6509 nan 0.1000 -0.0005
800 0.6497 nan 0.1000 -0.0012
820 0.6483 nan 0.1000 -0.0007
840 0.6462 nan 0.1000 -0.0002
860 0.6451 nan 0.1000 -0.0005
880 0.6439 nan 0.1000 -0.0010
900 0.6420 nan 0.1000 -0.0012
920 0.6415 nan 0.1000 -0.0008
940 0.6399 nan 0.1000 -0.0007
960 0.6381 nan 0.1000 -0.0014
980 0.6357 nan 0.1000 -0.0007
1000 0.6337 nan 0.1000 -0.0003
1020 0.6334 nan 0.1000 -0.0007
1040 0.6323 nan 0.1000 -0.0010
1060 0.6302 nan 0.1000 -0.0011
1080 0.6297 nan 0.1000 -0.0011
1100 0.6281 nan 0.1000 -0.0003
- Fold04.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2567 nan 0.1000 0.0319
2 1.1937 nan 0.1000 0.0294
3 1.1440 nan 0.1000 0.0238
4 1.1071 nan 0.1000 0.0200
5 1.0731 nan 0.1000 0.0162
6 1.0473 nan 0.1000 0.0128
7 1.0230 nan 0.1000 0.0125
8 0.9993 nan 0.1000 0.0102
9 0.9808 nan 0.1000 0.0088
10 0.9626 nan 0.1000 0.0080
20 0.8629 nan 0.1000 0.0014
40 0.7837 nan 0.1000 0.0001
60 0.7442 nan 0.1000 -0.0008
80 0.7149 nan 0.1000 -0.0004
100 0.6976 nan 0.1000 -0.0011
120 0.6790 nan 0.1000 -0.0010
140 0.6654 nan 0.1000 -0.0012
160 0.6515 nan 0.1000 -0.0014
180 0.6382 nan 0.1000 -0.0012
200 0.6252 nan 0.1000 -0.0010
220 0.6162 nan 0.1000 -0.0005
240 0.6055 nan 0.1000 -0.0007
260 0.5965 nan 0.1000 -0.0027
280 0.5872 nan 0.1000 -0.0010
300 0.5770 nan 0.1000 -0.0007
320 0.5657 nan 0.1000 -0.0011
340 0.5593 nan 0.1000 -0.0009
360 0.5520 nan 0.1000 -0.0011
380 0.5454 nan 0.1000 -0.0008
400 0.5408 nan 0.1000 -0.0022
420 0.5345 nan 0.1000 -0.0004
440 0.5274 nan 0.1000 -0.0012
460 0.5193 nan 0.1000 -0.0005
480 0.5128 nan 0.1000 -0.0002
500 0.5079 nan 0.1000 -0.0006
520 0.5036 nan 0.1000 -0.0011
540 0.4961 nan 0.1000 -0.0015
560 0.4900 nan 0.1000 -0.0009
580 0.4846 nan 0.1000 -0.0014
600 0.4786 nan 0.1000 -0.0010
620 0.4721 nan 0.1000 -0.0010
640 0.4686 nan 0.1000 -0.0012
660 0.4638 nan 0.1000 -0.0009
680 0.4591 nan 0.1000 -0.0008
700 0.4535 nan 0.1000 -0.0001
720 0.4509 nan 0.1000 -0.0014
740 0.4442 nan 0.1000 -0.0006
760 0.4390 nan 0.1000 -0.0006
780 0.4336 nan 0.1000 -0.0012
800 0.4312 nan 0.1000 -0.0006
820 0.4281 nan 0.1000 -0.0005
840 0.4239 nan 0.1000 -0.0009
860 0.4176 nan 0.1000 -0.0005
880 0.4150 nan 0.1000 -0.0011
900 0.4114 nan 0.1000 -0.0007
920 0.4084 nan 0.1000 -0.0010
940 0.4046 nan 0.1000 -0.0008
960 0.4013 nan 0.1000 -0.0014
980 0.3972 nan 0.1000 -0.0008
1000 0.3950 nan 0.1000 -0.0010
1020 0.3917 nan 0.1000 -0.0005
1040 0.3882 nan 0.1000 -0.0005
1060 0.3844 nan 0.1000 -0.0008
1080 0.3820 nan 0.1000 -0.0006
1100 0.3792 nan 0.1000 -0.0009
- Fold04.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2540 nan 0.1000 0.0406
2 1.1888 nan 0.1000 0.0309
3 1.1378 nan 0.1000 0.0240
4 1.0924 nan 0.1000 0.0218
5 1.0554 nan 0.1000 0.0185
6 1.0220 nan 0.1000 0.0173
7 0.9935 nan 0.1000 0.0125
8 0.9718 nan 0.1000 0.0110
9 0.9519 nan 0.1000 0.0092
10 0.9347 nan 0.1000 0.0080
20 0.8223 nan 0.1000 0.0019
40 0.7450 nan 0.1000 -0.0006
60 0.7005 nan 0.1000 0.0003
80 0.6679 nan 0.1000 -0.0008
100 0.6393 nan 0.1000 -0.0012
120 0.6187 nan 0.1000 -0.0013
140 0.5974 nan 0.1000 -0.0012
160 0.5777 nan 0.1000 -0.0012
180 0.5630 nan 0.1000 -0.0010
200 0.5491 nan 0.1000 -0.0015
220 0.5357 nan 0.1000 -0.0012
240 0.5201 nan 0.1000 -0.0008
260 0.5086 nan 0.1000 -0.0012
280 0.4967 nan 0.1000 -0.0012
300 0.4869 nan 0.1000 -0.0016
320 0.4757 nan 0.1000 -0.0014
340 0.4628 nan 0.1000 -0.0014
360 0.4545 nan 0.1000 -0.0009
380 0.4450 nan 0.1000 -0.0013
400 0.4367 nan 0.1000 -0.0014
420 0.4286 nan 0.1000 -0.0004
440 0.4208 nan 0.1000 -0.0010
460 0.4132 nan 0.1000 -0.0018
480 0.4072 nan 0.1000 -0.0011
500 0.3987 nan 0.1000 -0.0018
520 0.3898 nan 0.1000 -0.0006
540 0.3822 nan 0.1000 -0.0010
560 0.3750 nan 0.1000 -0.0020
580 0.3681 nan 0.1000 -0.0007
600 0.3611 nan 0.1000 -0.0006
620 0.3546 nan 0.1000 -0.0006
640 0.3497 nan 0.1000 -0.0008
660 0.3442 nan 0.1000 -0.0008
680 0.3386 nan 0.1000 -0.0007
700 0.3329 nan 0.1000 -0.0011
720 0.3274 nan 0.1000 -0.0005
740 0.3229 nan 0.1000 -0.0015
760 0.3184 nan 0.1000 -0.0008
780 0.3121 nan 0.1000 -0.0012
800 0.3088 nan 0.1000 -0.0016
820 0.3033 nan 0.1000 -0.0012
840 0.2987 nan 0.1000 -0.0003
860 0.2943 nan 0.1000 -0.0005
880 0.2902 nan 0.1000 -0.0008
900 0.2859 nan 0.1000 -0.0008
920 0.2821 nan 0.1000 -0.0005
940 0.2776 nan 0.1000 -0.0004
960 0.2750 nan 0.1000 -0.0013
980 0.2703 nan 0.1000 -0.0013
1000 0.2671 nan 0.1000 -0.0010
1020 0.2637 nan 0.1000 -0.0005
1040 0.2606 nan 0.1000 -0.0007
1060 0.2575 nan 0.1000 -0.0004
1080 0.2536 nan 0.1000 -0.0008
1100 0.2508 nan 0.1000 -0.0009
- Fold04.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3254 nan 0.0100 0.0028
2 1.3197 nan 0.0100 0.0028
3 1.3145 nan 0.0100 0.0028
4 1.3091 nan 0.0100 0.0028
5 1.3037 nan 0.0100 0.0026
6 1.2986 nan 0.0100 0.0026
7 1.2930 nan 0.0100 0.0025
8 1.2884 nan 0.0100 0.0025
9 1.2834 nan 0.0100 0.0025
10 1.2786 nan 0.0100 0.0024
20 1.2342 nan 0.0100 0.0020
40 1.1670 nan 0.0100 0.0014
60 1.1203 nan 0.0100 0.0010
80 1.0834 nan 0.0100 0.0007
100 1.0529 nan 0.0100 0.0006
120 1.0282 nan 0.0100 0.0005
140 1.0069 nan 0.0100 0.0003
160 0.9888 nan 0.0100 0.0003
180 0.9732 nan 0.0100 0.0002
200 0.9601 nan 0.0100 0.0003
220 0.9479 nan 0.0100 0.0002
240 0.9375 nan 0.0100 -0.0000
260 0.9278 nan 0.0100 0.0001
280 0.9194 nan 0.0100 0.0000
300 0.9114 nan 0.0100 0.0001
320 0.9045 nan 0.0100 0.0001
340 0.8989 nan 0.0100 0.0001
360 0.8930 nan 0.0100 0.0001
380 0.8878 nan 0.0100 0.0000
400 0.8831 nan 0.0100 0.0001
420 0.8782 nan 0.0100 0.0000
440 0.8741 nan 0.0100 0.0001
460 0.8697 nan 0.0100 -0.0000
480 0.8653 nan 0.0100 -0.0000
500 0.8610 nan 0.0100 -0.0000
520 0.8574 nan 0.0100 0.0001
540 0.8540 nan 0.0100 0.0000
560 0.8506 nan 0.0100 0.0000
580 0.8477 nan 0.0100 -0.0000
600 0.8448 nan 0.0100 -0.0000
620 0.8415 nan 0.0100 -0.0000
640 0.8388 nan 0.0100 0.0000
660 0.8360 nan 0.0100 0.0000
680 0.8335 nan 0.0100 -0.0000
700 0.8309 nan 0.0100 0.0000
720 0.8284 nan 0.0100 0.0000
740 0.8262 nan 0.0100 0.0000
760 0.8240 nan 0.0100 -0.0000
780 0.8218 nan 0.0100 -0.0001
800 0.8194 nan 0.0100 -0.0001
820 0.8175 nan 0.0100 -0.0002
840 0.8154 nan 0.0100 -0.0000
860 0.8134 nan 0.0100 0.0000
880 0.8115 nan 0.0100 -0.0001
900 0.8098 nan 0.0100 -0.0000
920 0.8077 nan 0.0100 -0.0000
940 0.8058 nan 0.0100 -0.0000
960 0.8038 nan 0.0100 -0.0001
980 0.8022 nan 0.0100 -0.0000
1000 0.8008 nan 0.0100 -0.0000
1020 0.7992 nan 0.0100 -0.0001
1040 0.7976 nan 0.0100 -0.0000
1060 0.7961 nan 0.0100 -0.0001
1080 0.7946 nan 0.0100 -0.0000
1100 0.7932 nan 0.0100 -0.0001
- Fold05.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0036
2 1.3169 nan 0.0100 0.0034
3 1.3099 nan 0.0100 0.0035
4 1.3031 nan 0.0100 0.0033
5 1.2966 nan 0.0100 0.0033
6 1.2902 nan 0.0100 0.0030
7 1.2839 nan 0.0100 0.0033
8 1.2773 nan 0.0100 0.0033
9 1.2712 nan 0.0100 0.0029
10 1.2655 nan 0.0100 0.0027
20 1.2094 nan 0.0100 0.0026
40 1.1232 nan 0.0100 0.0018
60 1.0598 nan 0.0100 0.0011
80 1.0124 nan 0.0100 0.0010
100 0.9753 nan 0.0100 0.0005
120 0.9470 nan 0.0100 0.0005
140 0.9236 nan 0.0100 0.0003
160 0.9045 nan 0.0100 0.0002
180 0.8884 nan 0.0100 0.0003
200 0.8754 nan 0.0100 0.0001
220 0.8636 nan 0.0100 0.0001
240 0.8536 nan 0.0100 0.0001
260 0.8445 nan 0.0100 0.0000
280 0.8349 nan 0.0100 0.0002
300 0.8272 nan 0.0100 0.0001
320 0.8206 nan 0.0100 -0.0001
340 0.8142 nan 0.0100 0.0001
360 0.8079 nan 0.0100 0.0001
380 0.8026 nan 0.0100 0.0001
400 0.7975 nan 0.0100 0.0000
420 0.7927 nan 0.0100 -0.0001
440 0.7883 nan 0.0100 -0.0000
460 0.7843 nan 0.0100 0.0000
480 0.7802 nan 0.0100 -0.0000
500 0.7760 nan 0.0100 -0.0001
520 0.7727 nan 0.0100 -0.0000
540 0.7693 nan 0.0100 -0.0001
560 0.7658 nan 0.0100 -0.0000
580 0.7623 nan 0.0100 -0.0000
600 0.7589 nan 0.0100 -0.0001
620 0.7559 nan 0.0100 -0.0001
640 0.7530 nan 0.0100 -0.0000
660 0.7503 nan 0.0100 -0.0001
680 0.7473 nan 0.0100 -0.0000
700 0.7443 nan 0.0100 -0.0001
720 0.7415 nan 0.0100 -0.0000
740 0.7394 nan 0.0100 -0.0001
760 0.7365 nan 0.0100 -0.0002
780 0.7337 nan 0.0100 -0.0001
800 0.7308 nan 0.0100 -0.0000
820 0.7283 nan 0.0100 -0.0002
840 0.7258 nan 0.0100 -0.0001
860 0.7238 nan 0.0100 -0.0001
880 0.7217 nan 0.0100 -0.0000
900 0.7196 nan 0.0100 -0.0001
920 0.7173 nan 0.0100 -0.0001
940 0.7151 nan 0.0100 -0.0001
960 0.7134 nan 0.0100 -0.0000
980 0.7112 nan 0.0100 -0.0001
1000 0.7094 nan 0.0100 -0.0001
1020 0.7072 nan 0.0100 -0.0001
1040 0.7053 nan 0.0100 -0.0001
1060 0.7038 nan 0.0100 -0.0000
1080 0.7022 nan 0.0100 -0.0000
1100 0.7005 nan 0.0100 -0.0000
- Fold05.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3230 nan 0.0100 0.0039
2 1.3143 nan 0.0100 0.0033
3 1.3068 nan 0.0100 0.0038
4 1.2992 nan 0.0100 0.0036
5 1.2915 nan 0.0100 0.0041
6 1.2841 nan 0.0100 0.0035
7 1.2771 nan 0.0100 0.0035
8 1.2697 nan 0.0100 0.0033
9 1.2626 nan 0.0100 0.0034
10 1.2559 nan 0.0100 0.0032
20 1.1942 nan 0.0100 0.0025
40 1.0995 nan 0.0100 0.0020
60 1.0289 nan 0.0100 0.0013
80 0.9777 nan 0.0100 0.0009
100 0.9371 nan 0.0100 0.0009
120 0.9067 nan 0.0100 0.0006
140 0.8827 nan 0.0100 0.0005
160 0.8625 nan 0.0100 0.0003
180 0.8466 nan 0.0100 0.0002
200 0.8325 nan 0.0100 0.0001
220 0.8212 nan 0.0100 0.0001
240 0.8105 nan 0.0100 -0.0001
260 0.8005 nan 0.0100 0.0001
280 0.7917 nan 0.0100 0.0001
300 0.7837 nan 0.0100 -0.0002
320 0.7764 nan 0.0100 0.0000
340 0.7701 nan 0.0100 -0.0001
360 0.7641 nan 0.0100 -0.0001
380 0.7581 nan 0.0100 -0.0000
400 0.7525 nan 0.0100 -0.0000
420 0.7470 nan 0.0100 0.0001
440 0.7416 nan 0.0100 -0.0001
460 0.7374 nan 0.0100 -0.0001
480 0.7328 nan 0.0100 -0.0000
500 0.7281 nan 0.0100 -0.0000
520 0.7240 nan 0.0100 -0.0001
540 0.7195 nan 0.0100 -0.0001
560 0.7157 nan 0.0100 0.0000
580 0.7120 nan 0.0100 -0.0001
600 0.7084 nan 0.0100 -0.0001
620 0.7043 nan 0.0100 -0.0003
640 0.7003 nan 0.0100 0.0000
660 0.6972 nan 0.0100 -0.0002
680 0.6940 nan 0.0100 -0.0002
700 0.6908 nan 0.0100 -0.0000
720 0.6872 nan 0.0100 -0.0001
740 0.6845 nan 0.0100 -0.0000
760 0.6816 nan 0.0100 -0.0002
780 0.6792 nan 0.0100 -0.0000
800 0.6760 nan 0.0100 -0.0000
820 0.6731 nan 0.0100 -0.0001
840 0.6707 nan 0.0100 -0.0002
860 0.6678 nan 0.0100 -0.0000
880 0.6650 nan 0.0100 -0.0001
900 0.6624 nan 0.0100 -0.0001
920 0.6596 nan 0.0100 -0.0001
940 0.6576 nan 0.0100 -0.0001
960 0.6546 nan 0.0100 -0.0001
980 0.6519 nan 0.0100 -0.0002
1000 0.6494 nan 0.0100 -0.0000
1020 0.6469 nan 0.0100 -0.0000
1040 0.6443 nan 0.0100 -0.0001
1060 0.6414 nan 0.0100 -0.0001
1080 0.6389 nan 0.0100 -0.0001
1100 0.6365 nan 0.0100 -0.0001
- Fold05.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2759 nan 0.1000 0.0269
2 1.2310 nan 0.1000 0.0210
3 1.1922 nan 0.1000 0.0185
4 1.1619 nan 0.1000 0.0144
5 1.1336 nan 0.1000 0.0125
6 1.1114 nan 0.1000 0.0102
7 1.0939 nan 0.1000 0.0083
8 1.0791 nan 0.1000 0.0064
9 1.0594 nan 0.1000 0.0078
10 1.0436 nan 0.1000 0.0070
20 0.9526 nan 0.1000 0.0029
40 0.8776 nan 0.1000 0.0007
60 0.8387 nan 0.1000 -0.0005
80 0.8164 nan 0.1000 -0.0007
100 0.7992 nan 0.1000 -0.0010
120 0.7859 nan 0.1000 -0.0010
140 0.7757 nan 0.1000 0.0000
160 0.7654 nan 0.1000 -0.0001
180 0.7580 nan 0.1000 -0.0007
200 0.7524 nan 0.1000 -0.0005
220 0.7439 nan 0.1000 -0.0012
240 0.7387 nan 0.1000 -0.0008
260 0.7330 nan 0.1000 -0.0012
280 0.7301 nan 0.1000 -0.0006
300 0.7267 nan 0.1000 -0.0004
320 0.7220 nan 0.1000 -0.0005
340 0.7186 nan 0.1000 -0.0010
360 0.7170 nan 0.1000 -0.0009
380 0.7128 nan 0.1000 -0.0004
400 0.7095 nan 0.1000 -0.0009
420 0.7080 nan 0.1000 -0.0009
440 0.7058 nan 0.1000 -0.0009
460 0.7031 nan 0.1000 -0.0007
480 0.7015 nan 0.1000 -0.0007
500 0.6992 nan 0.1000 -0.0004
520 0.6977 nan 0.1000 -0.0006
540 0.6949 nan 0.1000 -0.0007
560 0.6930 nan 0.1000 -0.0007
580 0.6909 nan 0.1000 -0.0009
600 0.6881 nan 0.1000 -0.0004
620 0.6869 nan 0.1000 -0.0008
640 0.6845 nan 0.1000 -0.0006
660 0.6816 nan 0.1000 -0.0010
680 0.6787 nan 0.1000 -0.0008
700 0.6768 nan 0.1000 -0.0007
720 0.6754 nan 0.1000 -0.0006
740 0.6731 nan 0.1000 -0.0007
760 0.6717 nan 0.1000 -0.0007
780 0.6700 nan 0.1000 -0.0008
800 0.6676 nan 0.1000 -0.0007
820 0.6668 nan 0.1000 -0.0012
840 0.6635 nan 0.1000 -0.0007
860 0.6612 nan 0.1000 -0.0006
880 0.6614 nan 0.1000 -0.0006
900 0.6597 nan 0.1000 -0.0007
920 0.6567 nan 0.1000 -0.0012
940 0.6557 nan 0.1000 -0.0009
960 0.6541 nan 0.1000 -0.0007
980 0.6518 nan 0.1000 -0.0009
1000 0.6497 nan 0.1000 -0.0004
1020 0.6484 nan 0.1000 -0.0005
1040 0.6476 nan 0.1000 -0.0008
1060 0.6456 nan 0.1000 -0.0006
1080 0.6446 nan 0.1000 -0.0008
1100 0.6419 nan 0.1000 -0.0006
- Fold05.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2608 nan 0.1000 0.0345
2 1.2007 nan 0.1000 0.0278
3 1.1551 nan 0.1000 0.0216
4 1.1142 nan 0.1000 0.0209
5 1.0817 nan 0.1000 0.0169
6 1.0516 nan 0.1000 0.0143
7 1.0255 nan 0.1000 0.0117
8 1.0009 nan 0.1000 0.0085
9 0.9819 nan 0.1000 0.0095
10 0.9654 nan 0.1000 0.0075
20 0.8778 nan 0.1000 0.0021
40 0.7967 nan 0.1000 0.0004
60 0.7605 nan 0.1000 -0.0020
80 0.7342 nan 0.1000 -0.0005
100 0.7139 nan 0.1000 -0.0009
120 0.6956 nan 0.1000 -0.0004
140 0.6757 nan 0.1000 -0.0009
160 0.6574 nan 0.1000 -0.0008
180 0.6433 nan 0.1000 -0.0008
200 0.6304 nan 0.1000 -0.0012
220 0.6194 nan 0.1000 -0.0012
240 0.6115 nan 0.1000 -0.0009
260 0.6026 nan 0.1000 -0.0003
280 0.5939 nan 0.1000 -0.0007
300 0.5844 nan 0.1000 -0.0017
320 0.5769 nan 0.1000 -0.0003
340 0.5698 nan 0.1000 -0.0011
360 0.5598 nan 0.1000 -0.0021
380 0.5524 nan 0.1000 -0.0013
400 0.5411 nan 0.1000 -0.0000
420 0.5362 nan 0.1000 -0.0009
440 0.5292 nan 0.1000 -0.0026
460 0.5238 nan 0.1000 -0.0016
480 0.5169 nan 0.1000 -0.0006
500 0.5105 nan 0.1000 -0.0013
520 0.5059 nan 0.1000 -0.0008
540 0.4992 nan 0.1000 -0.0013
560 0.4920 nan 0.1000 -0.0008
580 0.4874 nan 0.1000 -0.0016
600 0.4813 nan 0.1000 -0.0009
620 0.4745 nan 0.1000 -0.0010
640 0.4665 nan 0.1000 -0.0012
660 0.4597 nan 0.1000 -0.0010
680 0.4558 nan 0.1000 -0.0005
700 0.4513 nan 0.1000 -0.0004
720 0.4459 nan 0.1000 -0.0006
740 0.4420 nan 0.1000 -0.0007
760 0.4389 nan 0.1000 -0.0009
780 0.4340 nan 0.1000 -0.0015
800 0.4304 nan 0.1000 -0.0007
820 0.4259 nan 0.1000 -0.0003
840 0.4226 nan 0.1000 -0.0001
860 0.4174 nan 0.1000 -0.0004
880 0.4134 nan 0.1000 -0.0012
900 0.4096 nan 0.1000 -0.0008
920 0.4072 nan 0.1000 -0.0007
940 0.4022 nan 0.1000 -0.0008
960 0.3992 nan 0.1000 -0.0010
980 0.3965 nan 0.1000 -0.0007
1000 0.3923 nan 0.1000 -0.0006
1020 0.3904 nan 0.1000 -0.0007
1040 0.3880 nan 0.1000 -0.0011
1060 0.3854 nan 0.1000 -0.0010
1080 0.3814 nan 0.1000 -0.0009
1100 0.3777 nan 0.1000 -0.0012
- Fold05.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2552 nan 0.1000 0.0337
2 1.1903 nan 0.1000 0.0303
3 1.1356 nan 0.1000 0.0253
4 1.0917 nan 0.1000 0.0227
5 1.0573 nan 0.1000 0.0171
6 1.0233 nan 0.1000 0.0164
7 0.9950 nan 0.1000 0.0123
8 0.9684 nan 0.1000 0.0099
9 0.9469 nan 0.1000 0.0089
10 0.9275 nan 0.1000 0.0067
20 0.8306 nan 0.1000 0.0013
40 0.7533 nan 0.1000 -0.0010
60 0.7097 nan 0.1000 -0.0009
80 0.6829 nan 0.1000 -0.0004
100 0.6575 nan 0.1000 -0.0010
120 0.6346 nan 0.1000 -0.0017
140 0.6144 nan 0.1000 -0.0014
160 0.5922 nan 0.1000 -0.0003
180 0.5724 nan 0.1000 -0.0004
200 0.5578 nan 0.1000 -0.0020
220 0.5458 nan 0.1000 -0.0021
240 0.5266 nan 0.1000 -0.0009
260 0.5126 nan 0.1000 -0.0019
280 0.4998 nan 0.1000 -0.0007
300 0.4897 nan 0.1000 -0.0011
320 0.4780 nan 0.1000 -0.0004
340 0.4681 nan 0.1000 -0.0008
360 0.4582 nan 0.1000 -0.0005
380 0.4504 nan 0.1000 -0.0008
400 0.4429 nan 0.1000 -0.0009
420 0.4344 nan 0.1000 -0.0008
440 0.4254 nan 0.1000 -0.0011
460 0.4179 nan 0.1000 -0.0010
480 0.4102 nan 0.1000 -0.0009
500 0.4009 nan 0.1000 -0.0007
520 0.3929 nan 0.1000 -0.0007
540 0.3861 nan 0.1000 -0.0009
560 0.3766 nan 0.1000 -0.0008
580 0.3691 nan 0.1000 -0.0011
600 0.3627 nan 0.1000 -0.0007
620 0.3565 nan 0.1000 -0.0008
640 0.3499 nan 0.1000 -0.0005
660 0.3439 nan 0.1000 -0.0010
680 0.3402 nan 0.1000 -0.0009
700 0.3353 nan 0.1000 -0.0009
720 0.3289 nan 0.1000 -0.0013
740 0.3229 nan 0.1000 -0.0008
760 0.3176 nan 0.1000 -0.0013
780 0.3131 nan 0.1000 -0.0011
800 0.3078 nan 0.1000 -0.0005
820 0.3034 nan 0.1000 -0.0012
840 0.2991 nan 0.1000 -0.0004
860 0.2933 nan 0.1000 -0.0005
880 0.2888 nan 0.1000 -0.0013
900 0.2818 nan 0.1000 -0.0011
920 0.2781 nan 0.1000 -0.0006
940 0.2756 nan 0.1000 -0.0012
960 0.2711 nan 0.1000 -0.0006
980 0.2675 nan 0.1000 -0.0006
1000 0.2630 nan 0.1000 -0.0008
1020 0.2600 nan 0.1000 -0.0010
1040 0.2564 nan 0.1000 -0.0005
1060 0.2519 nan 0.1000 -0.0012
1080 0.2480 nan 0.1000 -0.0004
1100 0.2444 nan 0.1000 -0.0006
- Fold05.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3269 nan 0.0100 0.0028
2 1.3215 nan 0.0100 0.0027
3 1.3154 nan 0.0100 0.0026
4 1.3104 nan 0.0100 0.0026
5 1.3052 nan 0.0100 0.0025
6 1.2999 nan 0.0100 0.0025
7 1.2946 nan 0.0100 0.0025
8 1.2903 nan 0.0100 0.0024
9 1.2858 nan 0.0100 0.0024
10 1.2814 nan 0.0100 0.0023
20 1.2397 nan 0.0100 0.0019
40 1.1751 nan 0.0100 0.0008
60 1.1297 nan 0.0100 0.0009
80 1.0932 nan 0.0100 0.0008
100 1.0648 nan 0.0100 0.0006
120 1.0417 nan 0.0100 0.0005
140 1.0207 nan 0.0100 0.0003
160 1.0032 nan 0.0100 0.0003
180 0.9882 nan 0.0100 0.0003
200 0.9738 nan 0.0100 0.0002
220 0.9616 nan 0.0100 0.0002
240 0.9509 nan 0.0100 0.0002
260 0.9413 nan 0.0100 0.0001
280 0.9329 nan 0.0100 0.0000
300 0.9245 nan 0.0100 0.0001
320 0.9172 nan 0.0100 0.0001
340 0.9103 nan 0.0100 0.0001
360 0.9041 nan 0.0100 0.0001
380 0.8984 nan 0.0100 0.0001
400 0.8929 nan 0.0100 -0.0000
420 0.8882 nan 0.0100 -0.0000
440 0.8834 nan 0.0100 -0.0000
460 0.8787 nan 0.0100 0.0000
480 0.8742 nan 0.0100 0.0000
500 0.8704 nan 0.0100 0.0000
520 0.8665 nan 0.0100 0.0000
540 0.8629 nan 0.0100 0.0000
560 0.8596 nan 0.0100 0.0001
580 0.8564 nan 0.0100 0.0000
600 0.8538 nan 0.0100 0.0000
620 0.8509 nan 0.0100 -0.0000
640 0.8480 nan 0.0100 0.0000
660 0.8449 nan 0.0100 0.0000
680 0.8421 nan 0.0100 -0.0001
700 0.8393 nan 0.0100 0.0000
720 0.8369 nan 0.0100 -0.0001
740 0.8345 nan 0.0100 -0.0001
760 0.8321 nan 0.0100 -0.0000
780 0.8299 nan 0.0100 -0.0001
800 0.8277 nan 0.0100 -0.0000
820 0.8254 nan 0.0100 -0.0000
840 0.8237 nan 0.0100 -0.0001
860 0.8216 nan 0.0100 0.0000
880 0.8198 nan 0.0100 -0.0000
900 0.8178 nan 0.0100 -0.0000
920 0.8160 nan 0.0100 -0.0000
940 0.8146 nan 0.0100 -0.0001
960 0.8131 nan 0.0100 -0.0000
980 0.8116 nan 0.0100 -0.0001
1000 0.8099 nan 0.0100 0.0000
1020 0.8082 nan 0.0100 0.0000
1040 0.8065 nan 0.0100 0.0000
1060 0.8050 nan 0.0100 -0.0002
1080 0.8038 nan 0.0100 -0.0001
1100 0.8025 nan 0.0100 -0.0000
- Fold06.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3245 nan 0.0100 0.0035
2 1.3175 nan 0.0100 0.0034
3 1.3112 nan 0.0100 0.0032
4 1.3047 nan 0.0100 0.0031
5 1.2981 nan 0.0100 0.0034
6 1.2922 nan 0.0100 0.0033
7 1.2861 nan 0.0100 0.0031
8 1.2799 nan 0.0100 0.0030
9 1.2740 nan 0.0100 0.0029
10 1.2679 nan 0.0100 0.0032
20 1.2149 nan 0.0100 0.0026
40 1.1317 nan 0.0100 0.0016
60 1.0695 nan 0.0100 0.0013
80 1.0222 nan 0.0100 0.0010
100 0.9865 nan 0.0100 0.0008
120 0.9597 nan 0.0100 0.0006
140 0.9377 nan 0.0100 0.0004
160 0.9205 nan 0.0100 0.0001
180 0.9044 nan 0.0100 0.0002
200 0.8902 nan 0.0100 0.0002
220 0.8777 nan 0.0100 0.0002
240 0.8689 nan 0.0100 -0.0000
260 0.8597 nan 0.0100 0.0001
280 0.8518 nan 0.0100 0.0001
300 0.8444 nan 0.0100 0.0001
320 0.8379 nan 0.0100 0.0001
340 0.8306 nan 0.0100 0.0001
360 0.8241 nan 0.0100 0.0000
380 0.8181 nan 0.0100 -0.0001
400 0.8132 nan 0.0100 -0.0000
420 0.8089 nan 0.0100 -0.0000
440 0.8049 nan 0.0100 -0.0000
460 0.8003 nan 0.0100 -0.0000
480 0.7957 nan 0.0100 -0.0000
500 0.7918 nan 0.0100 -0.0001
520 0.7875 nan 0.0100 -0.0000
540 0.7843 nan 0.0100 -0.0001
560 0.7810 nan 0.0100 -0.0001
580 0.7774 nan 0.0100 0.0000
600 0.7738 nan 0.0100 0.0000
620 0.7710 nan 0.0100 -0.0002
640 0.7683 nan 0.0100 -0.0000
660 0.7653 nan 0.0100 -0.0001
680 0.7625 nan 0.0100 -0.0001
700 0.7602 nan 0.0100 -0.0001
720 0.7571 nan 0.0100 -0.0000
740 0.7544 nan 0.0100 0.0000
760 0.7523 nan 0.0100 -0.0001
780 0.7500 nan 0.0100 -0.0000
800 0.7472 nan 0.0100 -0.0001
820 0.7447 nan 0.0100 -0.0001
840 0.7421 nan 0.0100 -0.0001
860 0.7399 nan 0.0100 -0.0001
880 0.7377 nan 0.0100 -0.0000
900 0.7357 nan 0.0100 -0.0001
920 0.7337 nan 0.0100 -0.0001
940 0.7316 nan 0.0100 -0.0001
960 0.7289 nan 0.0100 0.0000
980 0.7270 nan 0.0100 -0.0002
1000 0.7253 nan 0.0100 -0.0000
1020 0.7231 nan 0.0100 -0.0001
1040 0.7212 nan 0.0100 -0.0002
1060 0.7192 nan 0.0100 -0.0000
1080 0.7170 nan 0.0100 -0.0001
1100 0.7152 nan 0.0100 -0.0001
- Fold06.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0033
2 1.3169 nan 0.0100 0.0037
3 1.3095 nan 0.0100 0.0036
4 1.3018 nan 0.0100 0.0037
5 1.2943 nan 0.0100 0.0036
6 1.2872 nan 0.0100 0.0035
7 1.2801 nan 0.0100 0.0035
8 1.2732 nan 0.0100 0.0033
9 1.2663 nan 0.0100 0.0033
10 1.2597 nan 0.0100 0.0033
20 1.1988 nan 0.0100 0.0026
40 1.1064 nan 0.0100 0.0018
60 1.0376 nan 0.0100 0.0013
80 0.9874 nan 0.0100 0.0009
100 0.9492 nan 0.0100 0.0008
120 0.9211 nan 0.0100 0.0003
140 0.8977 nan 0.0100 0.0006
160 0.8778 nan 0.0100 0.0002
180 0.8619 nan 0.0100 0.0001
200 0.8488 nan 0.0100 0.0000
220 0.8369 nan 0.0100 -0.0000
240 0.8257 nan 0.0100 -0.0001
260 0.8163 nan 0.0100 0.0001
280 0.8077 nan 0.0100 0.0001
300 0.8002 nan 0.0100 -0.0000
320 0.7928 nan 0.0100 0.0001
340 0.7856 nan 0.0100 0.0000
360 0.7790 nan 0.0100 -0.0001
380 0.7725 nan 0.0100 -0.0002
400 0.7667 nan 0.0100 0.0000
420 0.7613 nan 0.0100 0.0000
440 0.7562 nan 0.0100 -0.0000
460 0.7518 nan 0.0100 -0.0000
480 0.7466 nan 0.0100 -0.0001
500 0.7423 nan 0.0100 -0.0001
520 0.7379 nan 0.0100 0.0000
540 0.7340 nan 0.0100 -0.0001
560 0.7298 nan 0.0100 0.0000
580 0.7261 nan 0.0100 -0.0001
600 0.7226 nan 0.0100 -0.0001
620 0.7188 nan 0.0100 -0.0000
640 0.7148 nan 0.0100 0.0001
660 0.7108 nan 0.0100 -0.0001
680 0.7077 nan 0.0100 -0.0002
700 0.7038 nan 0.0100 -0.0001
720 0.7006 nan 0.0100 -0.0001
740 0.6978 nan 0.0100 -0.0001
760 0.6947 nan 0.0100 -0.0000
780 0.6917 nan 0.0100 -0.0001
800 0.6886 nan 0.0100 -0.0000
820 0.6860 nan 0.0100 -0.0001
840 0.6830 nan 0.0100 -0.0001
860 0.6805 nan 0.0100 -0.0001
880 0.6779 nan 0.0100 -0.0001
900 0.6754 nan 0.0100 -0.0002
920 0.6727 nan 0.0100 -0.0001
940 0.6699 nan 0.0100 -0.0001
960 0.6672 nan 0.0100 -0.0000
980 0.6652 nan 0.0100 -0.0001
1000 0.6626 nan 0.0100 -0.0001
1020 0.6603 nan 0.0100 -0.0001
1040 0.6582 nan 0.0100 -0.0001
1060 0.6559 nan 0.0100 -0.0002
1080 0.6531 nan 0.0100 -0.0002
1100 0.6506 nan 0.0100 -0.0001
- Fold06.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2800 nan 0.1000 0.0260
2 1.2347 nan 0.1000 0.0213
3 1.2020 nan 0.1000 0.0174
4 1.1721 nan 0.1000 0.0158
5 1.1447 nan 0.1000 0.0111
6 1.1230 nan 0.1000 0.0103
7 1.1066 nan 0.1000 0.0082
8 1.0889 nan 0.1000 0.0076
9 1.0759 nan 0.1000 0.0065
10 1.0623 nan 0.1000 0.0062
20 0.9697 nan 0.1000 0.0033
40 0.8932 nan 0.1000 0.0003
60 0.8530 nan 0.1000 -0.0005
80 0.8274 nan 0.1000 -0.0002
100 0.8105 nan 0.1000 -0.0007
120 0.7961 nan 0.1000 -0.0004
140 0.7861 nan 0.1000 -0.0012
160 0.7769 nan 0.1000 -0.0016
180 0.7680 nan 0.1000 -0.0000
200 0.7616 nan 0.1000 -0.0001
220 0.7551 nan 0.1000 -0.0002
240 0.7486 nan 0.1000 -0.0007
260 0.7422 nan 0.1000 -0.0008
280 0.7371 nan 0.1000 -0.0005
300 0.7333 nan 0.1000 -0.0005
320 0.7289 nan 0.1000 -0.0007
340 0.7259 nan 0.1000 -0.0005
360 0.7235 nan 0.1000 -0.0011
380 0.7202 nan 0.1000 -0.0008
400 0.7164 nan 0.1000 -0.0008
420 0.7122 nan 0.1000 -0.0001
440 0.7104 nan 0.1000 -0.0009
460 0.7086 nan 0.1000 -0.0013
480 0.7064 nan 0.1000 -0.0015
500 0.7026 nan 0.1000 -0.0008
520 0.7010 nan 0.1000 -0.0007
540 0.6991 nan 0.1000 -0.0009
560 0.6970 nan 0.1000 -0.0010
580 0.6936 nan 0.1000 -0.0011
600 0.6925 nan 0.1000 -0.0010
620 0.6893 nan 0.1000 -0.0011
640 0.6878 nan 0.1000 -0.0010
660 0.6851 nan 0.1000 -0.0005
680 0.6834 nan 0.1000 -0.0009
700 0.6827 nan 0.1000 -0.0005
720 0.6797 nan 0.1000 -0.0018
740 0.6784 nan 0.1000 -0.0016
760 0.6756 nan 0.1000 -0.0003
780 0.6745 nan 0.1000 -0.0007
800 0.6727 nan 0.1000 -0.0010
820 0.6713 nan 0.1000 -0.0010
840 0.6709 nan 0.1000 -0.0003
860 0.6686 nan 0.1000 -0.0016
880 0.6676 nan 0.1000 -0.0003
900 0.6659 nan 0.1000 -0.0003
920 0.6638 nan 0.1000 -0.0007
940 0.6630 nan 0.1000 -0.0007
960 0.6606 nan 0.1000 -0.0006
980 0.6591 nan 0.1000 -0.0005
1000 0.6577 nan 0.1000 -0.0008
1020 0.6560 nan 0.1000 -0.0006
1040 0.6544 nan 0.1000 -0.0007
1060 0.6535 nan 0.1000 -0.0014
1080 0.6526 nan 0.1000 -0.0003
1100 0.6518 nan 0.1000 -0.0004
- Fold06.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2645 nan 0.1000 0.0335
2 1.2041 nan 0.1000 0.0270
3 1.1587 nan 0.1000 0.0222
4 1.1173 nan 0.1000 0.0192
5 1.0863 nan 0.1000 0.0153
6 1.0561 nan 0.1000 0.0126
7 1.0340 nan 0.1000 0.0105
8 1.0100 nan 0.1000 0.0102
9 0.9927 nan 0.1000 0.0088
10 0.9750 nan 0.1000 0.0070
20 0.8821 nan 0.1000 0.0009
40 0.8152 nan 0.1000 -0.0003
60 0.7761 nan 0.1000 -0.0007
80 0.7518 nan 0.1000 -0.0005
100 0.7313 nan 0.1000 -0.0005
120 0.7120 nan 0.1000 -0.0007
140 0.6986 nan 0.1000 -0.0013
160 0.6801 nan 0.1000 -0.0009
180 0.6648 nan 0.1000 -0.0006
200 0.6506 nan 0.1000 -0.0008
220 0.6398 nan 0.1000 -0.0003
240 0.6269 nan 0.1000 -0.0008
260 0.6179 nan 0.1000 -0.0011
280 0.6071 nan 0.1000 -0.0012
300 0.5969 nan 0.1000 -0.0002
320 0.5894 nan 0.1000 -0.0008
340 0.5802 nan 0.1000 -0.0007
360 0.5730 nan 0.1000 -0.0015
380 0.5642 nan 0.1000 -0.0010
400 0.5580 nan 0.1000 -0.0012
420 0.5515 nan 0.1000 -0.0008
440 0.5413 nan 0.1000 -0.0013
460 0.5344 nan 0.1000 -0.0011
480 0.5296 nan 0.1000 -0.0009
500 0.5250 nan 0.1000 -0.0006
520 0.5185 nan 0.1000 -0.0010
540 0.5126 nan 0.1000 -0.0016
560 0.5071 nan 0.1000 -0.0000
580 0.5023 nan 0.1000 -0.0005
600 0.4977 nan 0.1000 -0.0008
620 0.4927 nan 0.1000 -0.0010
640 0.4880 nan 0.1000 -0.0005
660 0.4820 nan 0.1000 -0.0014
680 0.4796 nan 0.1000 -0.0003
700 0.4755 nan 0.1000 -0.0008
720 0.4712 nan 0.1000 -0.0010
740 0.4656 nan 0.1000 -0.0007
760 0.4622 nan 0.1000 -0.0011
780 0.4577 nan 0.1000 -0.0011
800 0.4530 nan 0.1000 -0.0007
820 0.4495 nan 0.1000 -0.0008
840 0.4445 nan 0.1000 -0.0011
860 0.4420 nan 0.1000 -0.0005
880 0.4384 nan 0.1000 -0.0006
900 0.4330 nan 0.1000 -0.0013
920 0.4303 nan 0.1000 -0.0006
940 0.4271 nan 0.1000 -0.0008
960 0.4239 nan 0.1000 -0.0007
980 0.4198 nan 0.1000 -0.0006
1000 0.4160 nan 0.1000 -0.0014
1020 0.4130 nan 0.1000 -0.0003
1040 0.4098 nan 0.1000 -0.0010
1060 0.4071 nan 0.1000 -0.0012
1080 0.4051 nan 0.1000 -0.0007
1100 0.4024 nan 0.1000 -0.0004
- Fold06.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2571 nan 0.1000 0.0348
2 1.1947 nan 0.1000 0.0302
3 1.1399 nan 0.1000 0.0257
4 1.0965 nan 0.1000 0.0204
5 1.0607 nan 0.1000 0.0177
6 1.0301 nan 0.1000 0.0155
7 1.0028 nan 0.1000 0.0121
8 0.9790 nan 0.1000 0.0083
9 0.9626 nan 0.1000 0.0072
10 0.9442 nan 0.1000 0.0070
20 0.8490 nan 0.1000 0.0011
40 0.7762 nan 0.1000 0.0004
60 0.7279 nan 0.1000 -0.0007
80 0.7030 nan 0.1000 -0.0007
100 0.6770 nan 0.1000 -0.0014
120 0.6516 nan 0.1000 -0.0022
140 0.6289 nan 0.1000 -0.0021
160 0.6130 nan 0.1000 -0.0014
180 0.5954 nan 0.1000 -0.0009
200 0.5764 nan 0.1000 -0.0008
220 0.5635 nan 0.1000 -0.0024
240 0.5482 nan 0.1000 -0.0012
260 0.5331 nan 0.1000 -0.0007
280 0.5173 nan 0.1000 -0.0009
300 0.5053 nan 0.1000 -0.0019
320 0.4937 nan 0.1000 -0.0009
340 0.4857 nan 0.1000 -0.0011
360 0.4758 nan 0.1000 -0.0015
380 0.4642 nan 0.1000 -0.0008
400 0.4570 nan 0.1000 -0.0009
420 0.4478 nan 0.1000 -0.0009
440 0.4392 nan 0.1000 -0.0008
460 0.4309 nan 0.1000 -0.0009
480 0.4232 nan 0.1000 -0.0012
500 0.4154 nan 0.1000 -0.0015
520 0.4075 nan 0.1000 -0.0008
540 0.4000 nan 0.1000 -0.0006
560 0.3945 nan 0.1000 -0.0011
580 0.3877 nan 0.1000 -0.0015
600 0.3826 nan 0.1000 -0.0013
620 0.3796 nan 0.1000 -0.0008
640 0.3704 nan 0.1000 -0.0008
660 0.3642 nan 0.1000 -0.0006
680 0.3576 nan 0.1000 -0.0009
700 0.3536 nan 0.1000 -0.0010
720 0.3490 nan 0.1000 -0.0009
740 0.3442 nan 0.1000 -0.0014
760 0.3372 nan 0.1000 -0.0008
780 0.3333 nan 0.1000 -0.0010
800 0.3275 nan 0.1000 -0.0005
820 0.3233 nan 0.1000 -0.0009
840 0.3204 nan 0.1000 -0.0010
860 0.3153 nan 0.1000 -0.0006
880 0.3107 nan 0.1000 -0.0011
900 0.3077 nan 0.1000 -0.0009
920 0.3024 nan 0.1000 -0.0006
940 0.2957 nan 0.1000 -0.0005
960 0.2911 nan 0.1000 -0.0005
980 0.2878 nan 0.1000 -0.0006
1000 0.2831 nan 0.1000 -0.0009
1020 0.2810 nan 0.1000 -0.0006
1040 0.2762 nan 0.1000 -0.0005
1060 0.2725 nan 0.1000 -0.0008
1080 0.2680 nan 0.1000 -0.0006
1100 0.2649 nan 0.1000 -0.0007
- Fold06.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3257 nan 0.0100 0.0031
2 1.3194 nan 0.0100 0.0030
3 1.3133 nan 0.0100 0.0029
4 1.3072 nan 0.0100 0.0028
5 1.3015 nan 0.0100 0.0028
6 1.2961 nan 0.0100 0.0028
7 1.2904 nan 0.0100 0.0027
8 1.2847 nan 0.0100 0.0026
9 1.2797 nan 0.0100 0.0026
10 1.2744 nan 0.0100 0.0025
20 1.2277 nan 0.0100 0.0021
40 1.1562 nan 0.0100 0.0014
60 1.1051 nan 0.0100 0.0010
80 1.0677 nan 0.0100 0.0008
100 1.0370 nan 0.0100 0.0006
120 1.0121 nan 0.0100 0.0005
140 0.9914 nan 0.0100 0.0004
160 0.9738 nan 0.0100 0.0002
180 0.9581 nan 0.0100 0.0002
200 0.9451 nan 0.0100 0.0002
220 0.9341 nan 0.0100 0.0003
240 0.9238 nan 0.0100 0.0002
260 0.9148 nan 0.0100 0.0001
280 0.9066 nan 0.0100 0.0002
300 0.8993 nan 0.0100 0.0001
320 0.8929 nan 0.0100 0.0001
340 0.8869 nan 0.0100 0.0001
360 0.8813 nan 0.0100 0.0001
380 0.8765 nan 0.0100 -0.0000
400 0.8713 nan 0.0100 0.0001
420 0.8668 nan 0.0100 0.0000
440 0.8623 nan 0.0100 0.0000
460 0.8582 nan 0.0100 -0.0000
480 0.8545 nan 0.0100 0.0000
500 0.8503 nan 0.0100 0.0000
520 0.8470 nan 0.0100 0.0000
540 0.8436 nan 0.0100 0.0000
560 0.8401 nan 0.0100 0.0000
580 0.8371 nan 0.0100 -0.0001
600 0.8338 nan 0.0100 -0.0000
620 0.8306 nan 0.0100 0.0000
640 0.8278 nan 0.0100 -0.0000
660 0.8252 nan 0.0100 -0.0000
680 0.8226 nan 0.0100 -0.0001
700 0.8201 nan 0.0100 0.0000
720 0.8179 nan 0.0100 -0.0001
740 0.8156 nan 0.0100 -0.0000
760 0.8135 nan 0.0100 -0.0001
780 0.8114 nan 0.0100 -0.0000
800 0.8092 nan 0.0100 -0.0000
820 0.8073 nan 0.0100 -0.0000
840 0.8054 nan 0.0100 -0.0000
860 0.8033 nan 0.0100 0.0000
880 0.8014 nan 0.0100 -0.0001
900 0.7997 nan 0.0100 -0.0002
920 0.7982 nan 0.0100 -0.0001
940 0.7966 nan 0.0100 -0.0000
960 0.7949 nan 0.0100 -0.0001
980 0.7933 nan 0.0100 -0.0001
1000 0.7918 nan 0.0100 0.0000
1020 0.7905 nan 0.0100 -0.0001
1040 0.7892 nan 0.0100 -0.0000
1060 0.7880 nan 0.0100 -0.0001
1080 0.7867 nan 0.0100 -0.0001
1100 0.7857 nan 0.0100 -0.0001
- Fold07.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3246 nan 0.0100 0.0037
2 1.3171 nan 0.0100 0.0036
3 1.3099 nan 0.0100 0.0035
4 1.3031 nan 0.0100 0.0037
5 1.2966 nan 0.0100 0.0031
6 1.2900 nan 0.0100 0.0034
7 1.2832 nan 0.0100 0.0030
8 1.2769 nan 0.0100 0.0033
9 1.2707 nan 0.0100 0.0027
10 1.2648 nan 0.0100 0.0030
20 1.2084 nan 0.0100 0.0023
40 1.1187 nan 0.0100 0.0018
60 1.0545 nan 0.0100 0.0013
80 1.0043 nan 0.0100 0.0010
100 0.9669 nan 0.0100 0.0007
120 0.9378 nan 0.0100 0.0005
140 0.9153 nan 0.0100 0.0004
160 0.8975 nan 0.0100 0.0003
180 0.8816 nan 0.0100 0.0004
200 0.8675 nan 0.0100 0.0000
220 0.8555 nan 0.0100 0.0001
240 0.8445 nan 0.0100 0.0000
260 0.8356 nan 0.0100 0.0001
280 0.8281 nan 0.0100 -0.0000
300 0.8210 nan 0.0100 0.0001
320 0.8138 nan 0.0100 -0.0000
340 0.8079 nan 0.0100 0.0000
360 0.8024 nan 0.0100 0.0000
380 0.7965 nan 0.0100 -0.0000
400 0.7909 nan 0.0100 -0.0000
420 0.7858 nan 0.0100 0.0001
440 0.7809 nan 0.0100 0.0000
460 0.7765 nan 0.0100 0.0000
480 0.7719 nan 0.0100 -0.0001
500 0.7680 nan 0.0100 0.0000
520 0.7638 nan 0.0100 -0.0000
540 0.7606 nan 0.0100 -0.0000
560 0.7575 nan 0.0100 -0.0000
580 0.7540 nan 0.0100 -0.0001
600 0.7508 nan 0.0100 -0.0000
620 0.7480 nan 0.0100 -0.0001
640 0.7454 nan 0.0100 -0.0001
660 0.7421 nan 0.0100 -0.0000
680 0.7393 nan 0.0100 -0.0001
700 0.7362 nan 0.0100 -0.0001
720 0.7339 nan 0.0100 -0.0001
740 0.7316 nan 0.0100 -0.0001
760 0.7292 nan 0.0100 -0.0001
780 0.7269 nan 0.0100 -0.0001
800 0.7243 nan 0.0100 -0.0001
820 0.7221 nan 0.0100 -0.0001
840 0.7197 nan 0.0100 -0.0001
860 0.7176 nan 0.0100 -0.0001
880 0.7157 nan 0.0100 -0.0001
900 0.7132 nan 0.0100 -0.0000
920 0.7110 nan 0.0100 -0.0000
940 0.7089 nan 0.0100 -0.0001
960 0.7067 nan 0.0100 -0.0001
980 0.7049 nan 0.0100 -0.0002
1000 0.7031 nan 0.0100 -0.0001
1020 0.7011 nan 0.0100 -0.0001
1040 0.6991 nan 0.0100 -0.0001
1060 0.6969 nan 0.0100 -0.0001
1080 0.6954 nan 0.0100 -0.0001
1100 0.6931 nan 0.0100 -0.0001
- Fold07.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3236 nan 0.0100 0.0040
2 1.3153 nan 0.0100 0.0037
3 1.3074 nan 0.0100 0.0037
4 1.2997 nan 0.0100 0.0038
5 1.2920 nan 0.0100 0.0037
6 1.2843 nan 0.0100 0.0035
7 1.2768 nan 0.0100 0.0034
8 1.2696 nan 0.0100 0.0035
9 1.2623 nan 0.0100 0.0035
10 1.2559 nan 0.0100 0.0033
20 1.1933 nan 0.0100 0.0028
40 1.0975 nan 0.0100 0.0019
60 1.0275 nan 0.0100 0.0014
80 0.9755 nan 0.0100 0.0011
100 0.9350 nan 0.0100 0.0006
120 0.9060 nan 0.0100 0.0006
140 0.8818 nan 0.0100 0.0004
160 0.8622 nan 0.0100 0.0002
180 0.8452 nan 0.0100 0.0003
200 0.8316 nan 0.0100 0.0000
220 0.8193 nan 0.0100 0.0001
240 0.8086 nan 0.0100 0.0001
260 0.7991 nan 0.0100 -0.0000
280 0.7908 nan 0.0100 -0.0001
300 0.7821 nan 0.0100 -0.0001
320 0.7749 nan 0.0100 -0.0000
340 0.7685 nan 0.0100 -0.0001
360 0.7625 nan 0.0100 0.0000
380 0.7569 nan 0.0100 0.0001
400 0.7504 nan 0.0100 -0.0000
420 0.7450 nan 0.0100 -0.0001
440 0.7402 nan 0.0100 -0.0000
460 0.7356 nan 0.0100 -0.0000
480 0.7312 nan 0.0100 0.0000
500 0.7272 nan 0.0100 -0.0003
520 0.7221 nan 0.0100 0.0000
540 0.7177 nan 0.0100 -0.0002
560 0.7138 nan 0.0100 -0.0001
580 0.7098 nan 0.0100 -0.0001
600 0.7064 nan 0.0100 -0.0000
620 0.7024 nan 0.0100 -0.0002
640 0.6988 nan 0.0100 -0.0002
660 0.6954 nan 0.0100 0.0000
680 0.6924 nan 0.0100 -0.0001
700 0.6888 nan 0.0100 -0.0002
720 0.6852 nan 0.0100 0.0000
740 0.6821 nan 0.0100 -0.0000
760 0.6786 nan 0.0100 0.0000
780 0.6757 nan 0.0100 -0.0001
800 0.6724 nan 0.0100 -0.0001
820 0.6695 nan 0.0100 -0.0001
840 0.6671 nan 0.0100 -0.0003
860 0.6637 nan 0.0100 -0.0001
880 0.6611 nan 0.0100 -0.0001
900 0.6584 nan 0.0100 -0.0001
920 0.6559 nan 0.0100 -0.0001
940 0.6534 nan 0.0100 -0.0000
960 0.6508 nan 0.0100 -0.0001
980 0.6481 nan 0.0100 -0.0001
1000 0.6455 nan 0.0100 -0.0001
1020 0.6433 nan 0.0100 -0.0001
1040 0.6411 nan 0.0100 -0.0002
1060 0.6389 nan 0.0100 -0.0002
1080 0.6366 nan 0.0100 -0.0001
1100 0.6341 nan 0.0100 -0.0002
- Fold07.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2737 nan 0.1000 0.0288
2 1.2198 nan 0.1000 0.0241
3 1.1821 nan 0.1000 0.0190
4 1.1486 nan 0.1000 0.0151
5 1.1284 nan 0.1000 0.0071
6 1.1023 nan 0.1000 0.0125
7 1.0794 nan 0.1000 0.0103
8 1.0637 nan 0.1000 0.0058
9 1.0460 nan 0.1000 0.0082
10 1.0317 nan 0.1000 0.0065
20 0.9422 nan 0.1000 0.0011
40 0.8725 nan 0.1000 0.0008
60 0.8347 nan 0.1000 -0.0009
80 0.8095 nan 0.1000 -0.0001
100 0.7927 nan 0.1000 -0.0005
120 0.7813 nan 0.1000 -0.0006
140 0.7695 nan 0.1000 -0.0004
160 0.7622 nan 0.1000 -0.0004
180 0.7552 nan 0.1000 -0.0006
200 0.7500 nan 0.1000 -0.0006
220 0.7431 nan 0.1000 -0.0004
240 0.7397 nan 0.1000 -0.0007
260 0.7348 nan 0.1000 -0.0005
280 0.7287 nan 0.1000 -0.0009
300 0.7242 nan 0.1000 -0.0005
320 0.7195 nan 0.1000 -0.0011
340 0.7158 nan 0.1000 -0.0005
360 0.7114 nan 0.1000 -0.0007
380 0.7072 nan 0.1000 -0.0005
400 0.7044 nan 0.1000 -0.0006
420 0.7008 nan 0.1000 -0.0002
440 0.6983 nan 0.1000 -0.0002
460 0.6970 nan 0.1000 -0.0004
480 0.6936 nan 0.1000 -0.0011
500 0.6910 nan 0.1000 -0.0004
520 0.6888 nan 0.1000 -0.0013
540 0.6875 nan 0.1000 -0.0008
560 0.6857 nan 0.1000 -0.0007
580 0.6840 nan 0.1000 -0.0007
600 0.6819 nan 0.1000 -0.0006
620 0.6803 nan 0.1000 -0.0009
640 0.6781 nan 0.1000 -0.0005
660 0.6762 nan 0.1000 -0.0005
680 0.6748 nan 0.1000 -0.0007
700 0.6734 nan 0.1000 -0.0010
720 0.6715 nan 0.1000 -0.0012
740 0.6701 nan 0.1000 -0.0003
760 0.6674 nan 0.1000 -0.0001
780 0.6658 nan 0.1000 -0.0009
800 0.6633 nan 0.1000 -0.0008
820 0.6609 nan 0.1000 -0.0008
840 0.6590 nan 0.1000 -0.0012
860 0.6575 nan 0.1000 -0.0007
880 0.6567 nan 0.1000 -0.0008
900 0.6556 nan 0.1000 -0.0012
920 0.6535 nan 0.1000 -0.0002
940 0.6528 nan 0.1000 -0.0004
960 0.6505 nan 0.1000 -0.0006
980 0.6505 nan 0.1000 -0.0008
1000 0.6478 nan 0.1000 -0.0009
1020 0.6460 nan 0.1000 -0.0004
1040 0.6435 nan 0.1000 -0.0009
1060 0.6430 nan 0.1000 -0.0011
1080 0.6418 nan 0.1000 -0.0006
1100 0.6407 nan 0.1000 -0.0007
- Fold07.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2600 nan 0.1000 0.0317
2 1.2054 nan 0.1000 0.0262
3 1.1511 nan 0.1000 0.0230
4 1.1099 nan 0.1000 0.0205
5 1.0752 nan 0.1000 0.0163
6 1.0420 nan 0.1000 0.0138
7 1.0182 nan 0.1000 0.0119
8 0.9941 nan 0.1000 0.0105
9 0.9734 nan 0.1000 0.0090
10 0.9583 nan 0.1000 0.0066
20 0.8608 nan 0.1000 0.0035
40 0.7920 nan 0.1000 -0.0004
60 0.7567 nan 0.1000 -0.0004
80 0.7305 nan 0.1000 -0.0000
100 0.7096 nan 0.1000 -0.0015
120 0.6896 nan 0.1000 -0.0002
140 0.6732 nan 0.1000 -0.0013
160 0.6587 nan 0.1000 -0.0007
180 0.6448 nan 0.1000 -0.0004
200 0.6304 nan 0.1000 -0.0016
220 0.6196 nan 0.1000 0.0002
240 0.6094 nan 0.1000 -0.0009
260 0.6011 nan 0.1000 -0.0010
280 0.5937 nan 0.1000 -0.0007
300 0.5806 nan 0.1000 -0.0013
320 0.5750 nan 0.1000 -0.0004
340 0.5673 nan 0.1000 -0.0013
360 0.5596 nan 0.1000 -0.0014
380 0.5523 nan 0.1000 -0.0013
400 0.5433 nan 0.1000 -0.0016
420 0.5369 nan 0.1000 -0.0009
440 0.5317 nan 0.1000 -0.0009
460 0.5243 nan 0.1000 -0.0007
480 0.5160 nan 0.1000 -0.0009
500 0.5078 nan 0.1000 -0.0007
520 0.5037 nan 0.1000 -0.0011
540 0.4969 nan 0.1000 -0.0007
560 0.4933 nan 0.1000 -0.0006
580 0.4901 nan 0.1000 -0.0010
600 0.4860 nan 0.1000 -0.0011
620 0.4805 nan 0.1000 -0.0016
640 0.4764 nan 0.1000 -0.0010
660 0.4723 nan 0.1000 -0.0007
680 0.4690 nan 0.1000 -0.0006
700 0.4637 nan 0.1000 -0.0012
720 0.4593 nan 0.1000 -0.0011
740 0.4560 nan 0.1000 -0.0003
760 0.4497 nan 0.1000 -0.0010
780 0.4443 nan 0.1000 -0.0010
800 0.4410 nan 0.1000 -0.0004
820 0.4379 nan 0.1000 -0.0010
840 0.4327 nan 0.1000 -0.0011
860 0.4302 nan 0.1000 -0.0016
880 0.4271 nan 0.1000 -0.0006
900 0.4237 nan 0.1000 -0.0005
920 0.4205 nan 0.1000 -0.0007
940 0.4176 nan 0.1000 -0.0005
960 0.4164 nan 0.1000 -0.0004
980 0.4129 nan 0.1000 -0.0010
1000 0.4083 nan 0.1000 -0.0006
1020 0.4060 nan 0.1000 -0.0007
1040 0.4029 nan 0.1000 -0.0005
1060 0.3998 nan 0.1000 -0.0011
1080 0.3960 nan 0.1000 -0.0004
1100 0.3942 nan 0.1000 -0.0012
- Fold07.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2527 nan 0.1000 0.0402
2 1.1868 nan 0.1000 0.0311
3 1.1335 nan 0.1000 0.0262
4 1.0895 nan 0.1000 0.0211
5 1.0524 nan 0.1000 0.0190
6 1.0201 nan 0.1000 0.0160
7 0.9909 nan 0.1000 0.0115
8 0.9636 nan 0.1000 0.0111
9 0.9436 nan 0.1000 0.0105
10 0.9258 nan 0.1000 0.0083
20 0.8287 nan 0.1000 0.0023
40 0.7498 nan 0.1000 -0.0016
60 0.7049 nan 0.1000 -0.0019
80 0.6696 nan 0.1000 -0.0014
100 0.6433 nan 0.1000 -0.0008
120 0.6252 nan 0.1000 -0.0009
140 0.6051 nan 0.1000 -0.0014
160 0.5895 nan 0.1000 -0.0011
180 0.5711 nan 0.1000 -0.0019
200 0.5539 nan 0.1000 -0.0013
220 0.5419 nan 0.1000 -0.0008
240 0.5260 nan 0.1000 -0.0013
260 0.5147 nan 0.1000 -0.0006
280 0.5027 nan 0.1000 -0.0028
300 0.4895 nan 0.1000 -0.0015
320 0.4802 nan 0.1000 -0.0009
340 0.4705 nan 0.1000 -0.0007
360 0.4615 nan 0.1000 -0.0004
380 0.4504 nan 0.1000 -0.0007
400 0.4420 nan 0.1000 -0.0017
420 0.4306 nan 0.1000 -0.0018
440 0.4214 nan 0.1000 -0.0003
460 0.4145 nan 0.1000 -0.0023
480 0.4078 nan 0.1000 -0.0008
500 0.4007 nan 0.1000 -0.0008
520 0.3930 nan 0.1000 -0.0011
540 0.3855 nan 0.1000 -0.0008
560 0.3803 nan 0.1000 -0.0005
580 0.3726 nan 0.1000 -0.0012
600 0.3658 nan 0.1000 -0.0017
620 0.3594 nan 0.1000 -0.0007
640 0.3547 nan 0.1000 -0.0010
660 0.3474 nan 0.1000 -0.0007
680 0.3435 nan 0.1000 -0.0010
700 0.3382 nan 0.1000 -0.0004
720 0.3332 nan 0.1000 -0.0009
740 0.3273 nan 0.1000 -0.0007
760 0.3242 nan 0.1000 -0.0008
780 0.3180 nan 0.1000 -0.0007
800 0.3142 nan 0.1000 -0.0002
820 0.3104 nan 0.1000 -0.0005
840 0.3076 nan 0.1000 -0.0006
860 0.3045 nan 0.1000 -0.0002
880 0.3006 nan 0.1000 -0.0007
900 0.2965 nan 0.1000 -0.0019
920 0.2927 nan 0.1000 -0.0004
940 0.2885 nan 0.1000 -0.0009
960 0.2832 nan 0.1000 -0.0003
980 0.2801 nan 0.1000 -0.0004
1000 0.2775 nan 0.1000 -0.0008
1020 0.2739 nan 0.1000 -0.0010
1040 0.2706 nan 0.1000 -0.0008
1060 0.2672 nan 0.1000 -0.0003
1080 0.2640 nan 0.1000 -0.0005
1100 0.2606 nan 0.1000 -0.0010
- Fold07.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3261 nan 0.0100 0.0028
2 1.3202 nan 0.0100 0.0029
3 1.3143 nan 0.0100 0.0028
4 1.3090 nan 0.0100 0.0026
5 1.3033 nan 0.0100 0.0027
6 1.2979 nan 0.0100 0.0026
7 1.2922 nan 0.0100 0.0024
8 1.2869 nan 0.0100 0.0026
9 1.2817 nan 0.0100 0.0025
10 1.2768 nan 0.0100 0.0023
20 1.2337 nan 0.0100 0.0019
40 1.1675 nan 0.0100 0.0013
60 1.1231 nan 0.0100 0.0008
80 1.0876 nan 0.0100 0.0007
100 1.0580 nan 0.0100 0.0004
120 1.0341 nan 0.0100 0.0005
140 1.0132 nan 0.0100 0.0004
160 0.9957 nan 0.0100 0.0003
180 0.9812 nan 0.0100 0.0002
200 0.9679 nan 0.0100 0.0001
220 0.9555 nan 0.0100 0.0002
240 0.9447 nan 0.0100 0.0002
260 0.9353 nan 0.0100 0.0001
280 0.9270 nan 0.0100 0.0002
300 0.9189 nan 0.0100 0.0001
320 0.9116 nan 0.0100 0.0001
340 0.9050 nan 0.0100 0.0000
360 0.8985 nan 0.0100 0.0001
380 0.8925 nan 0.0100 0.0001
400 0.8869 nan 0.0100 0.0001
420 0.8818 nan 0.0100 0.0000
440 0.8769 nan 0.0100 0.0001
460 0.8721 nan 0.0100 0.0000
480 0.8679 nan 0.0100 -0.0001
500 0.8641 nan 0.0100 0.0000
520 0.8600 nan 0.0100 0.0000
540 0.8564 nan 0.0100 0.0000
560 0.8528 nan 0.0100 0.0000
580 0.8491 nan 0.0100 0.0000
600 0.8457 nan 0.0100 0.0001
620 0.8424 nan 0.0100 -0.0000
640 0.8391 nan 0.0100 0.0000
660 0.8361 nan 0.0100 -0.0000
680 0.8334 nan 0.0100 0.0000
700 0.8306 nan 0.0100 0.0000
720 0.8279 nan 0.0100 0.0000
740 0.8253 nan 0.0100 -0.0000
760 0.8228 nan 0.0100 0.0001
780 0.8202 nan 0.0100 0.0000
800 0.8179 nan 0.0100 -0.0000
820 0.8158 nan 0.0100 -0.0001
840 0.8134 nan 0.0100 0.0000
860 0.8113 nan 0.0100 -0.0001
880 0.8091 nan 0.0100 -0.0000
900 0.8070 nan 0.0100 -0.0000
920 0.8052 nan 0.0100 -0.0000
940 0.8033 nan 0.0100 -0.0001
960 0.8015 nan 0.0100 0.0000
980 0.8001 nan 0.0100 -0.0000
1000 0.7981 nan 0.0100 -0.0001
1020 0.7966 nan 0.0100 -0.0001
1040 0.7950 nan 0.0100 -0.0000
1060 0.7932 nan 0.0100 -0.0000
1080 0.7917 nan 0.0100 -0.0001
1100 0.7903 nan 0.0100 0.0000
- Fold08.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3245 nan 0.0100 0.0038
2 1.3176 nan 0.0100 0.0036
3 1.3112 nan 0.0100 0.0030
4 1.3046 nan 0.0100 0.0033
5 1.2979 nan 0.0100 0.0033
6 1.2918 nan 0.0100 0.0034
7 1.2853 nan 0.0100 0.0031
8 1.2787 nan 0.0100 0.0032
9 1.2727 nan 0.0100 0.0031
10 1.2666 nan 0.0100 0.0028
20 1.2105 nan 0.0100 0.0025
40 1.1262 nan 0.0100 0.0017
60 1.0631 nan 0.0100 0.0012
80 1.0160 nan 0.0100 0.0009
100 0.9792 nan 0.0100 0.0008
120 0.9501 nan 0.0100 0.0004
140 0.9273 nan 0.0100 0.0003
160 0.9081 nan 0.0100 0.0001
180 0.8922 nan 0.0100 0.0003
200 0.8786 nan 0.0100 0.0002
220 0.8668 nan 0.0100 0.0001
240 0.8562 nan 0.0100 0.0002
260 0.8469 nan 0.0100 0.0000
280 0.8379 nan 0.0100 0.0001
300 0.8296 nan 0.0100 0.0001
320 0.8226 nan 0.0100 0.0000
340 0.8152 nan 0.0100 0.0000
360 0.8087 nan 0.0100 -0.0000
380 0.8027 nan 0.0100 -0.0000
400 0.7972 nan 0.0100 -0.0000
420 0.7924 nan 0.0100 -0.0001
440 0.7878 nan 0.0100 -0.0001
460 0.7832 nan 0.0100 -0.0001
480 0.7786 nan 0.0100 -0.0001
500 0.7747 nan 0.0100 -0.0000
520 0.7708 nan 0.0100 -0.0000
540 0.7673 nan 0.0100 -0.0001
560 0.7638 nan 0.0100 0.0001
580 0.7603 nan 0.0100 -0.0001
600 0.7570 nan 0.0100 -0.0000
620 0.7541 nan 0.0100 0.0001
640 0.7509 nan 0.0100 -0.0001
660 0.7479 nan 0.0100 -0.0001
680 0.7451 nan 0.0100 -0.0001
700 0.7421 nan 0.0100 -0.0001
720 0.7396 nan 0.0100 -0.0001
740 0.7372 nan 0.0100 -0.0001
760 0.7347 nan 0.0100 -0.0001
780 0.7321 nan 0.0100 -0.0001
800 0.7298 nan 0.0100 0.0000
820 0.7275 nan 0.0100 -0.0000
840 0.7255 nan 0.0100 -0.0001
860 0.7229 nan 0.0100 -0.0001
880 0.7207 nan 0.0100 0.0000
900 0.7184 nan 0.0100 -0.0000
920 0.7163 nan 0.0100 -0.0000
940 0.7145 nan 0.0100 -0.0001
960 0.7121 nan 0.0100 -0.0001
980 0.7100 nan 0.0100 -0.0001
1000 0.7081 nan 0.0100 -0.0002
1020 0.7056 nan 0.0100 -0.0001
1040 0.7037 nan 0.0100 -0.0000
1060 0.7015 nan 0.0100 -0.0001
1080 0.6995 nan 0.0100 -0.0001
1100 0.6981 nan 0.0100 -0.0001
- Fold08.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0041
2 1.3171 nan 0.0100 0.0036
3 1.3095 nan 0.0100 0.0035
4 1.3022 nan 0.0100 0.0038
5 1.2949 nan 0.0100 0.0036
6 1.2880 nan 0.0100 0.0036
7 1.2807 nan 0.0100 0.0037
8 1.2734 nan 0.0100 0.0034
9 1.2666 nan 0.0100 0.0033
10 1.2601 nan 0.0100 0.0032
20 1.1999 nan 0.0100 0.0027
40 1.1037 nan 0.0100 0.0019
60 1.0347 nan 0.0100 0.0014
80 0.9839 nan 0.0100 0.0010
100 0.9450 nan 0.0100 0.0004
120 0.9138 nan 0.0100 0.0006
140 0.8892 nan 0.0100 0.0003
160 0.8686 nan 0.0100 0.0002
180 0.8516 nan 0.0100 0.0002
200 0.8365 nan 0.0100 0.0003
220 0.8241 nan 0.0100 0.0001
240 0.8134 nan 0.0100 0.0000
260 0.8030 nan 0.0100 0.0001
280 0.7942 nan 0.0100 0.0000
300 0.7863 nan 0.0100 0.0000
320 0.7787 nan 0.0100 0.0001
340 0.7721 nan 0.0100 -0.0000
360 0.7652 nan 0.0100 -0.0001
380 0.7585 nan 0.0100 -0.0001
400 0.7530 nan 0.0100 -0.0000
420 0.7481 nan 0.0100 -0.0001
440 0.7432 nan 0.0100 -0.0000
460 0.7384 nan 0.0100 -0.0000
480 0.7338 nan 0.0100 0.0000
500 0.7294 nan 0.0100 -0.0001
520 0.7249 nan 0.0100 -0.0001
540 0.7203 nan 0.0100 -0.0001
560 0.7164 nan 0.0100 -0.0001
580 0.7124 nan 0.0100 -0.0001
600 0.7088 nan 0.0100 -0.0001
620 0.7055 nan 0.0100 -0.0001
640 0.7019 nan 0.0100 -0.0001
660 0.6987 nan 0.0100 -0.0002
680 0.6953 nan 0.0100 -0.0001
700 0.6921 nan 0.0100 -0.0000
720 0.6887 nan 0.0100 -0.0001
740 0.6861 nan 0.0100 -0.0001
760 0.6833 nan 0.0100 -0.0000
780 0.6805 nan 0.0100 -0.0002
800 0.6778 nan 0.0100 -0.0001
820 0.6755 nan 0.0100 0.0000
840 0.6729 nan 0.0100 -0.0002
860 0.6696 nan 0.0100 -0.0001
880 0.6666 nan 0.0100 -0.0001
900 0.6639 nan 0.0100 -0.0000
920 0.6612 nan 0.0100 -0.0001
940 0.6584 nan 0.0100 -0.0001
960 0.6558 nan 0.0100 0.0000
980 0.6535 nan 0.0100 -0.0001
1000 0.6507 nan 0.0100 -0.0000
1020 0.6483 nan 0.0100 -0.0000
1040 0.6456 nan 0.0100 -0.0001
1060 0.6434 nan 0.0100 -0.0001
1080 0.6412 nan 0.0100 -0.0001
1100 0.6387 nan 0.0100 -0.0001
- Fold08.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2773 nan 0.1000 0.0273
2 1.2345 nan 0.1000 0.0218
3 1.1992 nan 0.1000 0.0186
4 1.1681 nan 0.1000 0.0154
5 1.1422 nan 0.1000 0.0125
6 1.1218 nan 0.1000 0.0102
7 1.1047 nan 0.1000 0.0089
8 1.0877 nan 0.1000 0.0068
9 1.0713 nan 0.1000 0.0058
10 1.0574 nan 0.1000 0.0070
20 0.9649 nan 0.1000 0.0024
40 0.8868 nan 0.1000 -0.0001
60 0.8442 nan 0.1000 0.0003
80 0.8192 nan 0.1000 -0.0002
100 0.8001 nan 0.1000 -0.0001
120 0.7863 nan 0.1000 0.0002
140 0.7748 nan 0.1000 -0.0003
160 0.7643 nan 0.1000 -0.0007
180 0.7574 nan 0.1000 -0.0012
200 0.7509 nan 0.1000 -0.0009
220 0.7437 nan 0.1000 -0.0003
240 0.7377 nan 0.1000 -0.0005
260 0.7322 nan 0.1000 -0.0004
280 0.7265 nan 0.1000 -0.0011
300 0.7222 nan 0.1000 -0.0001
320 0.7192 nan 0.1000 -0.0005
340 0.7140 nan 0.1000 -0.0011
360 0.7100 nan 0.1000 -0.0003
380 0.7073 nan 0.1000 -0.0001
400 0.7040 nan 0.1000 -0.0008
420 0.6996 nan 0.1000 -0.0005
440 0.6966 nan 0.1000 -0.0011
460 0.6933 nan 0.1000 -0.0007
480 0.6908 nan 0.1000 -0.0005
500 0.6888 nan 0.1000 -0.0010
520 0.6836 nan 0.1000 -0.0008
540 0.6823 nan 0.1000 -0.0005
560 0.6810 nan 0.1000 -0.0006
580 0.6799 nan 0.1000 -0.0009
600 0.6775 nan 0.1000 -0.0008
620 0.6760 nan 0.1000 -0.0005
640 0.6747 nan 0.1000 -0.0005
660 0.6716 nan 0.1000 -0.0009
680 0.6701 nan 0.1000 -0.0004
700 0.6673 nan 0.1000 -0.0006
720 0.6655 nan 0.1000 -0.0009
740 0.6635 nan 0.1000 -0.0005
760 0.6621 nan 0.1000 -0.0000
780 0.6591 nan 0.1000 -0.0005
800 0.6578 nan 0.1000 -0.0003
820 0.6567 nan 0.1000 -0.0008
840 0.6557 nan 0.1000 -0.0015
860 0.6531 nan 0.1000 -0.0005
880 0.6528 nan 0.1000 -0.0019
900 0.6518 nan 0.1000 -0.0007
920 0.6502 nan 0.1000 -0.0005
940 0.6493 nan 0.1000 -0.0011
960 0.6477 nan 0.1000 -0.0007
980 0.6470 nan 0.1000 -0.0012
1000 0.6457 nan 0.1000 -0.0022
1020 0.6453 nan 0.1000 -0.0009
1040 0.6430 nan 0.1000 -0.0008
1060 0.6429 nan 0.1000 -0.0015
1080 0.6419 nan 0.1000 -0.0007
1100 0.6396 nan 0.1000 -0.0003
- Fold08.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2620 nan 0.1000 0.0341
2 1.2027 nan 0.1000 0.0269
3 1.1514 nan 0.1000 0.0233
4 1.1143 nan 0.1000 0.0188
5 1.0821 nan 0.1000 0.0135
6 1.0526 nan 0.1000 0.0131
7 1.0313 nan 0.1000 0.0110
8 1.0120 nan 0.1000 0.0086
9 0.9944 nan 0.1000 0.0099
10 0.9776 nan 0.1000 0.0071
20 0.8805 nan 0.1000 0.0016
40 0.7984 nan 0.1000 -0.0005
60 0.7613 nan 0.1000 -0.0008
80 0.7266 nan 0.1000 -0.0003
100 0.7071 nan 0.1000 -0.0005
120 0.6893 nan 0.1000 -0.0007
140 0.6716 nan 0.1000 -0.0010
160 0.6584 nan 0.1000 -0.0016
180 0.6447 nan 0.1000 -0.0007
200 0.6317 nan 0.1000 -0.0022
220 0.6199 nan 0.1000 -0.0012
240 0.6075 nan 0.1000 -0.0005
260 0.5976 nan 0.1000 -0.0009
280 0.5896 nan 0.1000 -0.0014
300 0.5824 nan 0.1000 -0.0012
320 0.5734 nan 0.1000 -0.0016
340 0.5645 nan 0.1000 -0.0013
360 0.5556 nan 0.1000 -0.0009
380 0.5472 nan 0.1000 -0.0013
400 0.5407 nan 0.1000 -0.0008
420 0.5368 nan 0.1000 -0.0008
440 0.5313 nan 0.1000 -0.0008
460 0.5262 nan 0.1000 -0.0010
480 0.5224 nan 0.1000 -0.0007
500 0.5178 nan 0.1000 -0.0010
520 0.5110 nan 0.1000 -0.0004
540 0.5059 nan 0.1000 -0.0004
560 0.5001 nan 0.1000 -0.0008
580 0.4947 nan 0.1000 -0.0007
600 0.4881 nan 0.1000 -0.0007
620 0.4807 nan 0.1000 -0.0018
640 0.4755 nan 0.1000 -0.0010
660 0.4717 nan 0.1000 -0.0003
680 0.4685 nan 0.1000 -0.0008
700 0.4647 nan 0.1000 -0.0014
720 0.4609 nan 0.1000 -0.0007
740 0.4564 nan 0.1000 -0.0005
760 0.4519 nan 0.1000 -0.0007
780 0.4491 nan 0.1000 -0.0009
800 0.4452 nan 0.1000 -0.0012
820 0.4414 nan 0.1000 -0.0004
840 0.4361 nan 0.1000 -0.0003
860 0.4340 nan 0.1000 -0.0013
880 0.4307 nan 0.1000 -0.0006
900 0.4267 nan 0.1000 -0.0004
920 0.4233 nan 0.1000 -0.0011
940 0.4190 nan 0.1000 -0.0012
960 0.4157 nan 0.1000 -0.0007
980 0.4120 nan 0.1000 -0.0005
1000 0.4090 nan 0.1000 -0.0008
1020 0.4059 nan 0.1000 -0.0004
1040 0.4019 nan 0.1000 -0.0014
1060 0.4003 nan 0.1000 -0.0004
1080 0.3973 nan 0.1000 -0.0008
1100 0.3941 nan 0.1000 -0.0012
- Fold08.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2554 nan 0.1000 0.0386
2 1.1970 nan 0.1000 0.0298
3 1.1458 nan 0.1000 0.0266
4 1.1021 nan 0.1000 0.0177
5 1.0658 nan 0.1000 0.0141
6 1.0341 nan 0.1000 0.0152
7 1.0050 nan 0.1000 0.0136
8 0.9807 nan 0.1000 0.0123
9 0.9590 nan 0.1000 0.0092
10 0.9436 nan 0.1000 0.0057
20 0.8394 nan 0.1000 0.0013
40 0.7604 nan 0.1000 -0.0013
60 0.7141 nan 0.1000 -0.0008
80 0.6813 nan 0.1000 -0.0017
100 0.6484 nan 0.1000 -0.0009
120 0.6276 nan 0.1000 -0.0017
140 0.6060 nan 0.1000 -0.0012
160 0.5884 nan 0.1000 -0.0020
180 0.5698 nan 0.1000 -0.0018
200 0.5552 nan 0.1000 -0.0023
220 0.5397 nan 0.1000 -0.0019
240 0.5283 nan 0.1000 -0.0013
260 0.5146 nan 0.1000 -0.0005
280 0.5023 nan 0.1000 -0.0006
300 0.4939 nan 0.1000 -0.0019
320 0.4867 nan 0.1000 -0.0012
340 0.4773 nan 0.1000 -0.0011
360 0.4695 nan 0.1000 -0.0022
380 0.4589 nan 0.1000 -0.0012
400 0.4523 nan 0.1000 -0.0011
420 0.4436 nan 0.1000 -0.0012
440 0.4336 nan 0.1000 -0.0006
460 0.4274 nan 0.1000 -0.0012
480 0.4184 nan 0.1000 -0.0005
500 0.4120 nan 0.1000 -0.0008
520 0.4049 nan 0.1000 -0.0007
540 0.4000 nan 0.1000 -0.0009
560 0.3938 nan 0.1000 -0.0005
580 0.3879 nan 0.1000 -0.0011
600 0.3819 nan 0.1000 -0.0011
620 0.3727 nan 0.1000 -0.0012
640 0.3683 nan 0.1000 -0.0012
660 0.3634 nan 0.1000 -0.0009
680 0.3578 nan 0.1000 -0.0004
700 0.3529 nan 0.1000 -0.0012
720 0.3464 nan 0.1000 -0.0008
740 0.3389 nan 0.1000 -0.0008
760 0.3335 nan 0.1000 -0.0010
780 0.3284 nan 0.1000 -0.0009
800 0.3259 nan 0.1000 -0.0012
820 0.3206 nan 0.1000 -0.0005
840 0.3150 nan 0.1000 -0.0010
860 0.3106 nan 0.1000 -0.0013
880 0.3069 nan 0.1000 -0.0007
900 0.3030 nan 0.1000 -0.0003
920 0.2989 nan 0.1000 -0.0005
940 0.2975 nan 0.1000 -0.0009
960 0.2930 nan 0.1000 -0.0008
980 0.2880 nan 0.1000 -0.0012
1000 0.2839 nan 0.1000 -0.0007
1020 0.2802 nan 0.1000 -0.0014
1040 0.2770 nan 0.1000 -0.0007
1060 0.2724 nan 0.1000 -0.0004
1080 0.2706 nan 0.1000 -0.0016
1100 0.2655 nan 0.1000 -0.0007
- Fold08.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3259 nan 0.0100 0.0031
2 1.3198 nan 0.0100 0.0029
3 1.3146 nan 0.0100 0.0029
4 1.3085 nan 0.0100 0.0029
5 1.3028 nan 0.0100 0.0027
6 1.2978 nan 0.0100 0.0028
7 1.2922 nan 0.0100 0.0026
8 1.2868 nan 0.0100 0.0027
9 1.2817 nan 0.0100 0.0025
10 1.2759 nan 0.0100 0.0026
20 1.2284 nan 0.0100 0.0020
40 1.1555 nan 0.0100 0.0014
60 1.1058 nan 0.0100 0.0010
80 1.0680 nan 0.0100 0.0008
100 1.0371 nan 0.0100 0.0006
120 1.0114 nan 0.0100 0.0005
140 0.9893 nan 0.0100 0.0004
160 0.9708 nan 0.0100 0.0003
180 0.9556 nan 0.0100 0.0003
200 0.9414 nan 0.0100 0.0003
220 0.9293 nan 0.0100 0.0001
240 0.9185 nan 0.0100 0.0002
260 0.9088 nan 0.0100 0.0001
280 0.8994 nan 0.0100 0.0001
300 0.8915 nan 0.0100 0.0001
320 0.8839 nan 0.0100 0.0001
340 0.8779 nan 0.0100 0.0001
360 0.8717 nan 0.0100 0.0001
380 0.8654 nan 0.0100 0.0001
400 0.8602 nan 0.0100 0.0001
420 0.8554 nan 0.0100 0.0001
440 0.8503 nan 0.0100 0.0001
460 0.8461 nan 0.0100 0.0001
480 0.8420 nan 0.0100 -0.0000
500 0.8385 nan 0.0100 0.0000
520 0.8349 nan 0.0100 -0.0000
540 0.8316 nan 0.0100 0.0001
560 0.8281 nan 0.0100 0.0000
580 0.8250 nan 0.0100 -0.0001
600 0.8221 nan 0.0100 -0.0000
620 0.8189 nan 0.0100 -0.0000
640 0.8164 nan 0.0100 0.0000
660 0.8137 nan 0.0100 0.0000
680 0.8111 nan 0.0100 -0.0000
700 0.8088 nan 0.0100 0.0000
720 0.8063 nan 0.0100 -0.0000
740 0.8039 nan 0.0100 -0.0001
760 0.8017 nan 0.0100 -0.0000
780 0.7993 nan 0.0100 0.0000
800 0.7972 nan 0.0100 0.0000
820 0.7950 nan 0.0100 -0.0000
840 0.7927 nan 0.0100 0.0000
860 0.7909 nan 0.0100 -0.0001
880 0.7890 nan 0.0100 -0.0001
900 0.7873 nan 0.0100 -0.0000
920 0.7854 nan 0.0100 -0.0001
940 0.7837 nan 0.0100 0.0000
960 0.7823 nan 0.0100 -0.0001
980 0.7807 nan 0.0100 -0.0000
1000 0.7789 nan 0.0100 -0.0000
1020 0.7772 nan 0.0100 -0.0001
1040 0.7755 nan 0.0100 -0.0001
1060 0.7741 nan 0.0100 -0.0001
1080 0.7727 nan 0.0100 0.0000
1100 0.7716 nan 0.0100 -0.0000
- Fold09.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3247 nan 0.0100 0.0037
2 1.3170 nan 0.0100 0.0037
3 1.3093 nan 0.0100 0.0037
4 1.3022 nan 0.0100 0.0033
5 1.2952 nan 0.0100 0.0035
6 1.2884 nan 0.0100 0.0032
7 1.2822 nan 0.0100 0.0031
8 1.2762 nan 0.0100 0.0032
9 1.2692 nan 0.0100 0.0032
10 1.2630 nan 0.0100 0.0033
20 1.2041 nan 0.0100 0.0027
40 1.1134 nan 0.0100 0.0018
60 1.0479 nan 0.0100 0.0013
80 0.9978 nan 0.0100 0.0010
100 0.9594 nan 0.0100 0.0007
120 0.9289 nan 0.0100 0.0006
140 0.9050 nan 0.0100 0.0005
160 0.8863 nan 0.0100 0.0001
180 0.8702 nan 0.0100 0.0001
200 0.8561 nan 0.0100 0.0001
220 0.8443 nan 0.0100 0.0001
240 0.8334 nan 0.0100 0.0001
260 0.8248 nan 0.0100 0.0002
280 0.8170 nan 0.0100 -0.0001
300 0.8094 nan 0.0100 0.0000
320 0.8023 nan 0.0100 0.0000
340 0.7957 nan 0.0100 0.0001
360 0.7900 nan 0.0100 -0.0001
380 0.7844 nan 0.0100 -0.0000
400 0.7792 nan 0.0100 0.0001
420 0.7741 nan 0.0100 -0.0000
440 0.7695 nan 0.0100 0.0001
460 0.7652 nan 0.0100 -0.0001
480 0.7612 nan 0.0100 -0.0001
500 0.7574 nan 0.0100 -0.0001
520 0.7537 nan 0.0100 0.0000
540 0.7503 nan 0.0100 0.0000
560 0.7474 nan 0.0100 -0.0001
580 0.7441 nan 0.0100 0.0001
600 0.7407 nan 0.0100 -0.0000
620 0.7377 nan 0.0100 -0.0001
640 0.7349 nan 0.0100 -0.0002
660 0.7318 nan 0.0100 -0.0002
680 0.7291 nan 0.0100 -0.0001
700 0.7269 nan 0.0100 -0.0000
720 0.7244 nan 0.0100 -0.0001
740 0.7219 nan 0.0100 -0.0000
760 0.7195 nan 0.0100 0.0000
780 0.7174 nan 0.0100 -0.0002
800 0.7151 nan 0.0100 -0.0001
820 0.7127 nan 0.0100 -0.0001
840 0.7104 nan 0.0100 -0.0001
860 0.7079 nan 0.0100 -0.0000
880 0.7059 nan 0.0100 -0.0001
900 0.7034 nan 0.0100 -0.0001
920 0.7013 nan 0.0100 -0.0000
940 0.6994 nan 0.0100 -0.0001
960 0.6975 nan 0.0100 -0.0001
980 0.6954 nan 0.0100 -0.0001
1000 0.6932 nan 0.0100 -0.0001
1020 0.6913 nan 0.0100 -0.0001
1040 0.6894 nan 0.0100 -0.0001
1060 0.6873 nan 0.0100 -0.0000
1080 0.6852 nan 0.0100 -0.0002
1100 0.6836 nan 0.0100 -0.0002
- Fold09.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3239 nan 0.0100 0.0039
2 1.3153 nan 0.0100 0.0040
3 1.3069 nan 0.0100 0.0039
4 1.2992 nan 0.0100 0.0037
5 1.2921 nan 0.0100 0.0038
6 1.2840 nan 0.0100 0.0035
7 1.2765 nan 0.0100 0.0037
8 1.2693 nan 0.0100 0.0034
9 1.2619 nan 0.0100 0.0036
10 1.2550 nan 0.0100 0.0036
20 1.1936 nan 0.0100 0.0027
40 1.0956 nan 0.0100 0.0017
60 1.0229 nan 0.0100 0.0014
80 0.9689 nan 0.0100 0.0011
100 0.9278 nan 0.0100 0.0008
120 0.8951 nan 0.0100 0.0005
140 0.8696 nan 0.0100 0.0004
160 0.8497 nan 0.0100 0.0003
180 0.8338 nan 0.0100 0.0001
200 0.8189 nan 0.0100 0.0001
220 0.8067 nan 0.0100 0.0001
240 0.7961 nan 0.0100 -0.0000
260 0.7865 nan 0.0100 0.0001
280 0.7772 nan 0.0100 0.0002
300 0.7698 nan 0.0100 -0.0001
320 0.7629 nan 0.0100 0.0001
340 0.7566 nan 0.0100 -0.0001
360 0.7501 nan 0.0100 0.0001
380 0.7447 nan 0.0100 -0.0001
400 0.7392 nan 0.0100 0.0001
420 0.7335 nan 0.0100 -0.0001
440 0.7284 nan 0.0100 -0.0001
460 0.7236 nan 0.0100 0.0000
480 0.7189 nan 0.0100 -0.0001
500 0.7143 nan 0.0100 -0.0001
520 0.7101 nan 0.0100 -0.0001
540 0.7066 nan 0.0100 -0.0001
560 0.7022 nan 0.0100 -0.0001
580 0.6984 nan 0.0100 -0.0001
600 0.6943 nan 0.0100 -0.0000
620 0.6906 nan 0.0100 -0.0001
640 0.6870 nan 0.0100 -0.0001
660 0.6839 nan 0.0100 -0.0000
680 0.6808 nan 0.0100 -0.0001
700 0.6777 nan 0.0100 -0.0001
720 0.6746 nan 0.0100 -0.0002
740 0.6714 nan 0.0100 -0.0001
760 0.6681 nan 0.0100 0.0000
780 0.6648 nan 0.0100 -0.0001
800 0.6621 nan 0.0100 -0.0001
820 0.6590 nan 0.0100 -0.0003
840 0.6563 nan 0.0100 -0.0001
860 0.6535 nan 0.0100 -0.0000
880 0.6504 nan 0.0100 -0.0001
900 0.6482 nan 0.0100 -0.0001
920 0.6452 nan 0.0100 -0.0002
940 0.6429 nan 0.0100 -0.0001
960 0.6405 nan 0.0100 -0.0001
980 0.6380 nan 0.0100 -0.0001
1000 0.6359 nan 0.0100 -0.0000
1020 0.6333 nan 0.0100 -0.0002
1040 0.6308 nan 0.0100 -0.0001
1060 0.6287 nan 0.0100 -0.0001
1080 0.6267 nan 0.0100 -0.0002
1100 0.6246 nan 0.0100 -0.0001
- Fold09.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2780 nan 0.1000 0.0293
2 1.2315 nan 0.1000 0.0240
3 1.1873 nan 0.1000 0.0190
4 1.1564 nan 0.1000 0.0171
5 1.1273 nan 0.1000 0.0134
6 1.1022 nan 0.1000 0.0112
7 1.0813 nan 0.1000 0.0087
8 1.0659 nan 0.1000 0.0077
9 1.0483 nan 0.1000 0.0085
10 1.0341 nan 0.1000 0.0066
20 0.9393 nan 0.1000 0.0019
40 0.8614 nan 0.1000 0.0002
60 0.8261 nan 0.1000 -0.0005
80 0.7963 nan 0.1000 -0.0000
100 0.7793 nan 0.1000 -0.0000
120 0.7648 nan 0.1000 -0.0004
140 0.7513 nan 0.1000 -0.0002
160 0.7435 nan 0.1000 -0.0008
180 0.7359 nan 0.1000 -0.0008
200 0.7318 nan 0.1000 -0.0005
220 0.7248 nan 0.1000 -0.0011
240 0.7180 nan 0.1000 -0.0003
260 0.7134 nan 0.1000 -0.0003
280 0.7084 nan 0.1000 -0.0004
300 0.7045 nan 0.1000 -0.0004
320 0.7013 nan 0.1000 -0.0007
340 0.6983 nan 0.1000 -0.0009
360 0.6945 nan 0.1000 -0.0009
380 0.6931 nan 0.1000 -0.0016
400 0.6905 nan 0.1000 -0.0008
420 0.6879 nan 0.1000 -0.0012
440 0.6850 nan 0.1000 -0.0006
460 0.6816 nan 0.1000 -0.0006
480 0.6792 nan 0.1000 -0.0004
500 0.6778 nan 0.1000 -0.0008
520 0.6745 nan 0.1000 -0.0004
540 0.6726 nan 0.1000 -0.0008
560 0.6707 nan 0.1000 -0.0018
580 0.6693 nan 0.1000 -0.0006
600 0.6684 nan 0.1000 -0.0013
620 0.6657 nan 0.1000 -0.0013
640 0.6644 nan 0.1000 -0.0008
660 0.6618 nan 0.1000 -0.0006
680 0.6603 nan 0.1000 -0.0008
700 0.6594 nan 0.1000 -0.0004
720 0.6577 nan 0.1000 -0.0006
740 0.6555 nan 0.1000 -0.0002
760 0.6534 nan 0.1000 -0.0013
780 0.6520 nan 0.1000 -0.0011
800 0.6510 nan 0.1000 -0.0004
820 0.6503 nan 0.1000 -0.0010
840 0.6483 nan 0.1000 -0.0005
860 0.6478 nan 0.1000 -0.0009
880 0.6472 nan 0.1000 -0.0013
900 0.6448 nan 0.1000 -0.0007
920 0.6437 nan 0.1000 -0.0007
940 0.6417 nan 0.1000 -0.0008
960 0.6402 nan 0.1000 -0.0004
980 0.6377 nan 0.1000 -0.0013
1000 0.6361 nan 0.1000 -0.0008
1020 0.6349 nan 0.1000 -0.0005
1040 0.6333 nan 0.1000 -0.0004
1060 0.6326 nan 0.1000 -0.0003
1080 0.6318 nan 0.1000 -0.0012
1100 0.6308 nan 0.1000 -0.0008
- Fold09.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2591 nan 0.1000 0.0336
2 1.1987 nan 0.1000 0.0312
3 1.1488 nan 0.1000 0.0243
4 1.1070 nan 0.1000 0.0195
5 1.0700 nan 0.1000 0.0161
6 1.0379 nan 0.1000 0.0151
7 1.0082 nan 0.1000 0.0121
8 0.9867 nan 0.1000 0.0106
9 0.9676 nan 0.1000 0.0083
10 0.9505 nan 0.1000 0.0068
20 0.8488 nan 0.1000 0.0015
40 0.7764 nan 0.1000 -0.0011
60 0.7408 nan 0.1000 -0.0007
80 0.7147 nan 0.1000 -0.0008
100 0.6931 nan 0.1000 -0.0006
120 0.6703 nan 0.1000 -0.0005
140 0.6541 nan 0.1000 -0.0025
160 0.6440 nan 0.1000 -0.0017
180 0.6318 nan 0.1000 -0.0010
200 0.6231 nan 0.1000 -0.0008
220 0.6132 nan 0.1000 -0.0012
240 0.6017 nan 0.1000 -0.0011
260 0.5908 nan 0.1000 -0.0011
280 0.5787 nan 0.1000 -0.0000
300 0.5717 nan 0.1000 -0.0013
320 0.5630 nan 0.1000 -0.0008
340 0.5557 nan 0.1000 -0.0018
360 0.5473 nan 0.1000 -0.0005
380 0.5397 nan 0.1000 -0.0012
400 0.5304 nan 0.1000 -0.0006
420 0.5219 nan 0.1000 0.0000
440 0.5150 nan 0.1000 -0.0022
460 0.5098 nan 0.1000 -0.0008
480 0.5041 nan 0.1000 -0.0005
500 0.4988 nan 0.1000 -0.0006
520 0.4921 nan 0.1000 -0.0012
540 0.4861 nan 0.1000 -0.0005
560 0.4814 nan 0.1000 -0.0012
580 0.4774 nan 0.1000 -0.0005
600 0.4726 nan 0.1000 -0.0007
620 0.4689 nan 0.1000 -0.0015
640 0.4660 nan 0.1000 -0.0007
660 0.4613 nan 0.1000 -0.0007
680 0.4573 nan 0.1000 -0.0007
700 0.4537 nan 0.1000 -0.0010
720 0.4490 nan 0.1000 -0.0003
740 0.4458 nan 0.1000 -0.0011
760 0.4411 nan 0.1000 -0.0010
780 0.4381 nan 0.1000 -0.0008
800 0.4326 nan 0.1000 -0.0006
820 0.4285 nan 0.1000 -0.0009
840 0.4239 nan 0.1000 -0.0009
860 0.4207 nan 0.1000 -0.0008
880 0.4170 nan 0.1000 -0.0019
900 0.4123 nan 0.1000 -0.0005
920 0.4089 nan 0.1000 -0.0012
940 0.4051 nan 0.1000 -0.0006
960 0.4012 nan 0.1000 -0.0010
980 0.3994 nan 0.1000 -0.0010
1000 0.3971 nan 0.1000 -0.0003
1020 0.3928 nan 0.1000 -0.0007
1040 0.3891 nan 0.1000 -0.0008
1060 0.3857 nan 0.1000 -0.0008
1080 0.3815 nan 0.1000 -0.0008
1100 0.3791 nan 0.1000 -0.0011
- Fold09.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2583 nan 0.1000 0.0384
2 1.1913 nan 0.1000 0.0321
3 1.1359 nan 0.1000 0.0271
4 1.0922 nan 0.1000 0.0224
5 1.0529 nan 0.1000 0.0169
6 1.0177 nan 0.1000 0.0159
7 0.9893 nan 0.1000 0.0117
8 0.9629 nan 0.1000 0.0121
9 0.9384 nan 0.1000 0.0100
10 0.9176 nan 0.1000 0.0081
20 0.8185 nan 0.1000 0.0014
40 0.7369 nan 0.1000 -0.0003
60 0.7072 nan 0.1000 -0.0006
80 0.6741 nan 0.1000 -0.0004
100 0.6434 nan 0.1000 -0.0006
120 0.6211 nan 0.1000 -0.0011
140 0.6053 nan 0.1000 -0.0017
160 0.5874 nan 0.1000 -0.0007
180 0.5728 nan 0.1000 -0.0012
200 0.5530 nan 0.1000 -0.0010
220 0.5382 nan 0.1000 -0.0016
240 0.5274 nan 0.1000 -0.0008
260 0.5130 nan 0.1000 -0.0010
280 0.5021 nan 0.1000 -0.0011
300 0.4898 nan 0.1000 -0.0009
320 0.4766 nan 0.1000 -0.0012
340 0.4695 nan 0.1000 -0.0013
360 0.4603 nan 0.1000 -0.0007
380 0.4494 nan 0.1000 -0.0016
400 0.4410 nan 0.1000 -0.0006
420 0.4301 nan 0.1000 -0.0011
440 0.4236 nan 0.1000 -0.0015
460 0.4151 nan 0.1000 -0.0017
480 0.4054 nan 0.1000 -0.0019
500 0.3997 nan 0.1000 -0.0011
520 0.3915 nan 0.1000 -0.0015
540 0.3854 nan 0.1000 -0.0010
560 0.3791 nan 0.1000 -0.0012
580 0.3736 nan 0.1000 -0.0004
600 0.3680 nan 0.1000 -0.0009
620 0.3623 nan 0.1000 -0.0008
640 0.3559 nan 0.1000 -0.0012
660 0.3507 nan 0.1000 -0.0010
680 0.3456 nan 0.1000 -0.0007
700 0.3401 nan 0.1000 -0.0015
720 0.3347 nan 0.1000 -0.0008
740 0.3286 nan 0.1000 -0.0004
760 0.3234 nan 0.1000 -0.0008
780 0.3187 nan 0.1000 -0.0007
800 0.3137 nan 0.1000 -0.0020
820 0.3084 nan 0.1000 -0.0007
840 0.3040 nan 0.1000 -0.0015
860 0.2994 nan 0.1000 -0.0009
880 0.2956 nan 0.1000 -0.0005
900 0.2911 nan 0.1000 -0.0018
920 0.2878 nan 0.1000 -0.0008
940 0.2841 nan 0.1000 -0.0009
960 0.2815 nan 0.1000 -0.0006
980 0.2767 nan 0.1000 -0.0006
1000 0.2731 nan 0.1000 -0.0009
1020 0.2696 nan 0.1000 -0.0013
1040 0.2669 nan 0.1000 -0.0008
1060 0.2636 nan 0.1000 -0.0010
1080 0.2604 nan 0.1000 -0.0012
1100 0.2576 nan 0.1000 -0.0007
- Fold09.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3270 nan 0.0100 0.0028
2 1.3214 nan 0.0100 0.0029
3 1.3155 nan 0.0100 0.0026
4 1.3102 nan 0.0100 0.0028
5 1.3048 nan 0.0100 0.0027
6 1.2993 nan 0.0100 0.0026
7 1.2938 nan 0.0100 0.0025
8 1.2892 nan 0.0100 0.0025
9 1.2840 nan 0.0100 0.0025
10 1.2796 nan 0.0100 0.0022
20 1.2353 nan 0.0100 0.0021
40 1.1682 nan 0.0100 0.0013
60 1.1202 nan 0.0100 0.0010
80 1.0848 nan 0.0100 0.0007
100 1.0561 nan 0.0100 0.0005
120 1.0310 nan 0.0100 0.0005
140 1.0107 nan 0.0100 0.0003
160 0.9927 nan 0.0100 0.0003
180 0.9776 nan 0.0100 0.0003
200 0.9654 nan 0.0100 0.0003
220 0.9536 nan 0.0100 0.0002
240 0.9435 nan 0.0100 0.0002
260 0.9341 nan 0.0100 0.0001
280 0.9262 nan 0.0100 0.0000
300 0.9184 nan 0.0100 0.0002
320 0.9118 nan 0.0100 0.0000
340 0.9051 nan 0.0100 0.0001
360 0.8994 nan 0.0100 0.0001
380 0.8936 nan 0.0100 0.0001
400 0.8884 nan 0.0100 0.0000
420 0.8838 nan 0.0100 -0.0001
440 0.8794 nan 0.0100 -0.0000
460 0.8752 nan 0.0100 0.0000
480 0.8713 nan 0.0100 0.0000
500 0.8676 nan 0.0100 -0.0001
520 0.8637 nan 0.0100 -0.0000
540 0.8602 nan 0.0100 -0.0000
560 0.8571 nan 0.0100 0.0000
580 0.8536 nan 0.0100 -0.0000
600 0.8506 nan 0.0100 -0.0000
620 0.8479 nan 0.0100 -0.0000
640 0.8451 nan 0.0100 0.0000
660 0.8425 nan 0.0100 0.0000
680 0.8400 nan 0.0100 -0.0000
700 0.8375 nan 0.0100 -0.0001
720 0.8354 nan 0.0100 -0.0000
740 0.8331 nan 0.0100 -0.0000
760 0.8309 nan 0.0100 -0.0001
780 0.8288 nan 0.0100 -0.0001
800 0.8267 nan 0.0100 -0.0000
820 0.8246 nan 0.0100 -0.0000
840 0.8225 nan 0.0100 0.0000
860 0.8208 nan 0.0100 -0.0000
880 0.8189 nan 0.0100 -0.0001
900 0.8172 nan 0.0100 0.0000
920 0.8155 nan 0.0100 -0.0000
940 0.8138 nan 0.0100 -0.0000
960 0.8122 nan 0.0100 -0.0000
980 0.8108 nan 0.0100 -0.0000
1000 0.8092 nan 0.0100 -0.0001
1020 0.8077 nan 0.0100 -0.0001
1040 0.8062 nan 0.0100 -0.0001
1060 0.8047 nan 0.0100 -0.0001
1080 0.8035 nan 0.0100 -0.0001
1100 0.8023 nan 0.0100 -0.0000
- Fold10.Rep3: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3253 nan 0.0100 0.0034
2 1.3184 nan 0.0100 0.0035
3 1.3113 nan 0.0100 0.0034
4 1.3050 nan 0.0100 0.0034
5 1.2984 nan 0.0100 0.0034
6 1.2917 nan 0.0100 0.0033
7 1.2853 nan 0.0100 0.0029
8 1.2788 nan 0.0100 0.0031
9 1.2725 nan 0.0100 0.0030
10 1.2663 nan 0.0100 0.0027
20 1.2106 nan 0.0100 0.0024
40 1.1253 nan 0.0100 0.0017
60 1.0628 nan 0.0100 0.0013
80 1.0163 nan 0.0100 0.0009
100 0.9798 nan 0.0100 0.0007
120 0.9507 nan 0.0100 0.0005
140 0.9275 nan 0.0100 0.0004
160 0.9094 nan 0.0100 0.0002
180 0.8943 nan 0.0100 0.0004
200 0.8817 nan 0.0100 0.0001
220 0.8703 nan 0.0100 0.0002
240 0.8605 nan 0.0100 -0.0000
260 0.8523 nan 0.0100 0.0001
280 0.8444 nan 0.0100 0.0001
300 0.8366 nan 0.0100 0.0001
320 0.8295 nan 0.0100 0.0001
340 0.8229 nan 0.0100 0.0001
360 0.8166 nan 0.0100 0.0001
380 0.8113 nan 0.0100 0.0001
400 0.8057 nan 0.0100 -0.0000
420 0.8010 nan 0.0100 -0.0000
440 0.7964 nan 0.0100 0.0000
460 0.7922 nan 0.0100 -0.0000
480 0.7883 nan 0.0100 -0.0001
500 0.7843 nan 0.0100 -0.0001
520 0.7805 nan 0.0100 -0.0000
540 0.7766 nan 0.0100 -0.0000
560 0.7734 nan 0.0100 -0.0000
580 0.7707 nan 0.0100 0.0000
600 0.7674 nan 0.0100 -0.0001
620 0.7644 nan 0.0100 -0.0001
640 0.7613 nan 0.0100 -0.0000
660 0.7582 nan 0.0100 -0.0001
680 0.7556 nan 0.0100 -0.0002
700 0.7530 nan 0.0100 -0.0002
720 0.7506 nan 0.0100 -0.0001
740 0.7481 nan 0.0100 -0.0001
760 0.7462 nan 0.0100 -0.0001
780 0.7434 nan 0.0100 -0.0000
800 0.7412 nan 0.0100 -0.0002
820 0.7390 nan 0.0100 -0.0000
840 0.7368 nan 0.0100 -0.0001
860 0.7347 nan 0.0100 -0.0001
880 0.7324 nan 0.0100 -0.0001
900 0.7303 nan 0.0100 -0.0001
920 0.7277 nan 0.0100 -0.0001
940 0.7252 nan 0.0100 -0.0001
960 0.7230 nan 0.0100 -0.0000
980 0.7209 nan 0.0100 -0.0000
1000 0.7192 nan 0.0100 0.0000
1020 0.7171 nan 0.0100 -0.0001
1040 0.7155 nan 0.0100 -0.0001
1060 0.7136 nan 0.0100 -0.0000
1080 0.7115 nan 0.0100 -0.0000
1100 0.7099 nan 0.0100 -0.0001
- Fold10.Rep3: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3241 nan 0.0100 0.0039
2 1.3155 nan 0.0100 0.0038
3 1.3080 nan 0.0100 0.0036
4 1.3004 nan 0.0100 0.0035
5 1.2932 nan 0.0100 0.0035
6 1.2857 nan 0.0100 0.0035
7 1.2787 nan 0.0100 0.0034
8 1.2715 nan 0.0100 0.0034
9 1.2642 nan 0.0100 0.0034
10 1.2573 nan 0.0100 0.0032
20 1.1971 nan 0.0100 0.0026
40 1.1035 nan 0.0100 0.0017
60 1.0342 nan 0.0100 0.0014
80 0.9835 nan 0.0100 0.0011
100 0.9446 nan 0.0100 0.0008
120 0.9149 nan 0.0100 0.0005
140 0.8919 nan 0.0100 0.0003
160 0.8723 nan 0.0100 0.0003
180 0.8564 nan 0.0100 0.0003
200 0.8419 nan 0.0100 0.0002
220 0.8301 nan 0.0100 0.0002
240 0.8188 nan 0.0100 0.0001
260 0.8088 nan 0.0100 0.0000
280 0.7996 nan 0.0100 0.0002
300 0.7910 nan 0.0100 0.0000
320 0.7841 nan 0.0100 -0.0001
340 0.7773 nan 0.0100 -0.0000
360 0.7717 nan 0.0100 -0.0000
380 0.7661 nan 0.0100 -0.0000
400 0.7605 nan 0.0100 -0.0001
420 0.7558 nan 0.0100 0.0000
440 0.7505 nan 0.0100 -0.0000
460 0.7452 nan 0.0100 -0.0001
480 0.7412 nan 0.0100 -0.0002
500 0.7368 nan 0.0100 -0.0000
520 0.7328 nan 0.0100 -0.0001
540 0.7281 nan 0.0100 -0.0001
560 0.7231 nan 0.0100 -0.0001
580 0.7191 nan 0.0100 0.0000
600 0.7155 nan 0.0100 -0.0001
620 0.7119 nan 0.0100 -0.0002
640 0.7086 nan 0.0100 -0.0000
660 0.7054 nan 0.0100 -0.0001
680 0.7020 nan 0.0100 -0.0001
700 0.6984 nan 0.0100 -0.0001
720 0.6955 nan 0.0100 -0.0001
740 0.6923 nan 0.0100 -0.0000
760 0.6893 nan 0.0100 -0.0001
780 0.6859 nan 0.0100 -0.0001
800 0.6830 nan 0.0100 -0.0002
820 0.6801 nan 0.0100 -0.0001
840 0.6776 nan 0.0100 -0.0001
860 0.6750 nan 0.0100 -0.0002
880 0.6721 nan 0.0100 -0.0001
900 0.6688 nan 0.0100 -0.0001
920 0.6666 nan 0.0100 -0.0001
940 0.6642 nan 0.0100 -0.0001
960 0.6620 nan 0.0100 -0.0002
980 0.6597 nan 0.0100 -0.0002
1000 0.6575 nan 0.0100 -0.0000
1020 0.6553 nan 0.0100 -0.0001
1040 0.6525 nan 0.0100 -0.0000
1060 0.6501 nan 0.0100 -0.0002
1080 0.6479 nan 0.0100 -0.0002
1100 0.6453 nan 0.0100 -0.0000
- Fold10.Rep3: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2755 nan 0.1000 0.0280
2 1.2291 nan 0.1000 0.0227
3 1.1916 nan 0.1000 0.0182
4 1.1601 nan 0.1000 0.0147
5 1.1321 nan 0.1000 0.0113
6 1.1083 nan 0.1000 0.0100
7 1.0897 nan 0.1000 0.0053
8 1.0741 nan 0.1000 0.0080
9 1.0593 nan 0.1000 0.0065
10 1.0433 nan 0.1000 0.0051
20 0.9604 nan 0.1000 0.0005
40 0.8860 nan 0.1000 -0.0000
60 0.8505 nan 0.1000 -0.0000
80 0.8267 nan 0.1000 -0.0006
100 0.8080 nan 0.1000 -0.0003
120 0.7949 nan 0.1000 -0.0006
140 0.7860 nan 0.1000 -0.0005
160 0.7787 nan 0.1000 -0.0006
180 0.7736 nan 0.1000 -0.0009
200 0.7661 nan 0.1000 -0.0001
220 0.7611 nan 0.1000 -0.0008
240 0.7534 nan 0.1000 -0.0004
260 0.7491 nan 0.1000 -0.0007
280 0.7430 nan 0.1000 -0.0008
300 0.7384 nan 0.1000 -0.0006
320 0.7361 nan 0.1000 -0.0005
340 0.7332 nan 0.1000 -0.0010
360 0.7289 nan 0.1000 -0.0003
380 0.7257 nan 0.1000 -0.0002
400 0.7218 nan 0.1000 -0.0004
420 0.7195 nan 0.1000 -0.0012
440 0.7164 nan 0.1000 -0.0002
460 0.7148 nan 0.1000 -0.0008
480 0.7124 nan 0.1000 -0.0005
500 0.7095 nan 0.1000 -0.0015
520 0.7071 nan 0.1000 -0.0007
540 0.7035 nan 0.1000 -0.0005
560 0.7008 nan 0.1000 -0.0008
580 0.6988 nan 0.1000 -0.0005
600 0.6955 nan 0.1000 -0.0003
620 0.6946 nan 0.1000 -0.0004
640 0.6934 nan 0.1000 -0.0009
660 0.6925 nan 0.1000 -0.0018
680 0.6897 nan 0.1000 -0.0010
700 0.6891 nan 0.1000 -0.0011
720 0.6855 nan 0.1000 -0.0013
740 0.6833 nan 0.1000 -0.0014
760 0.6823 nan 0.1000 -0.0014
780 0.6803 nan 0.1000 -0.0004
800 0.6779 nan 0.1000 -0.0005
820 0.6758 nan 0.1000 -0.0007
840 0.6736 nan 0.1000 -0.0002
860 0.6715 nan 0.1000 -0.0007
880 0.6698 nan 0.1000 -0.0007
900 0.6672 nan 0.1000 -0.0006
920 0.6663 nan 0.1000 -0.0008
940 0.6649 nan 0.1000 -0.0011
960 0.6635 nan 0.1000 -0.0009
980 0.6620 nan 0.1000 -0.0008
1000 0.6607 nan 0.1000 -0.0003
1020 0.6607 nan 0.1000 -0.0011
1040 0.6591 nan 0.1000 -0.0003
1060 0.6573 nan 0.1000 -0.0007
1080 0.6562 nan 0.1000 -0.0010
1100 0.6548 nan 0.1000 -0.0008
- Fold10.Rep3: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2652 nan 0.1000 0.0336
2 1.2109 nan 0.1000 0.0274
3 1.1668 nan 0.1000 0.0239
4 1.1284 nan 0.1000 0.0201
5 1.0954 nan 0.1000 0.0158
6 1.0619 nan 0.1000 0.0146
7 1.0367 nan 0.1000 0.0101
8 1.0129 nan 0.1000 0.0099
9 0.9944 nan 0.1000 0.0095
10 0.9775 nan 0.1000 0.0076
20 0.8814 nan 0.1000 0.0012
40 0.8099 nan 0.1000 -0.0005
60 0.7730 nan 0.1000 -0.0013
80 0.7457 nan 0.1000 -0.0008
100 0.7252 nan 0.1000 -0.0005
120 0.7053 nan 0.1000 -0.0016
140 0.6875 nan 0.1000 -0.0003
160 0.6744 nan 0.1000 -0.0004
180 0.6607 nan 0.1000 -0.0014
200 0.6463 nan 0.1000 -0.0014
220 0.6349 nan 0.1000 -0.0007
240 0.6242 nan 0.1000 -0.0016
260 0.6095 nan 0.1000 -0.0007
280 0.6003 nan 0.1000 -0.0001
300 0.5921 nan 0.1000 -0.0013
320 0.5825 nan 0.1000 -0.0012
340 0.5750 nan 0.1000 -0.0013
360 0.5677 nan 0.1000 -0.0008
380 0.5632 nan 0.1000 -0.0012
400 0.5560 nan 0.1000 -0.0012
420 0.5484 nan 0.1000 -0.0010
440 0.5409 nan 0.1000 -0.0012
460 0.5328 nan 0.1000 -0.0016
480 0.5251 nan 0.1000 -0.0005
500 0.5175 nan 0.1000 -0.0010
520 0.5109 nan 0.1000 -0.0006
540 0.5040 nan 0.1000 -0.0016
560 0.4966 nan 0.1000 -0.0011
580 0.4910 nan 0.1000 -0.0011
600 0.4869 nan 0.1000 -0.0013
620 0.4809 nan 0.1000 -0.0010
640 0.4769 nan 0.1000 -0.0010
660 0.4727 nan 0.1000 -0.0012
680 0.4694 nan 0.1000 -0.0005
700 0.4647 nan 0.1000 -0.0005
720 0.4606 nan 0.1000 -0.0007
740 0.4560 nan 0.1000 -0.0017
760 0.4546 nan 0.1000 -0.0006
780 0.4500 nan 0.1000 -0.0006
800 0.4454 nan 0.1000 -0.0006
820 0.4419 nan 0.1000 -0.0008
840 0.4389 nan 0.1000 -0.0008
860 0.4352 nan 0.1000 -0.0011
880 0.4326 nan 0.1000 -0.0005
900 0.4274 nan 0.1000 -0.0005
920 0.4245 nan 0.1000 -0.0008
940 0.4218 nan 0.1000 -0.0008
960 0.4177 nan 0.1000 -0.0008
980 0.4141 nan 0.1000 -0.0005
1000 0.4102 nan 0.1000 -0.0003
1020 0.4077 nan 0.1000 -0.0007
1040 0.4051 nan 0.1000 -0.0005
1060 0.4004 nan 0.1000 -0.0005
1080 0.3979 nan 0.1000 -0.0002
1100 0.3944 nan 0.1000 -0.0011
- Fold10.Rep3: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2569 nan 0.1000 0.0366
2 1.1927 nan 0.1000 0.0313
3 1.1385 nan 0.1000 0.0254
4 1.0968 nan 0.1000 0.0203
5 1.0619 nan 0.1000 0.0156
6 1.0267 nan 0.1000 0.0154
7 0.9989 nan 0.1000 0.0117
8 0.9776 nan 0.1000 0.0107
9 0.9575 nan 0.1000 0.0088
10 0.9381 nan 0.1000 0.0065
20 0.8440 nan 0.1000 0.0017
40 0.7599 nan 0.1000 0.0005
60 0.7125 nan 0.1000 -0.0005
80 0.6792 nan 0.1000 -0.0005
100 0.6563 nan 0.1000 -0.0005
120 0.6305 nan 0.1000 -0.0007
140 0.6112 nan 0.1000 -0.0010
160 0.5924 nan 0.1000 -0.0006
180 0.5747 nan 0.1000 -0.0014
200 0.5599 nan 0.1000 -0.0011
220 0.5473 nan 0.1000 -0.0013
240 0.5339 nan 0.1000 -0.0007
260 0.5193 nan 0.1000 -0.0010
280 0.5053 nan 0.1000 -0.0004
300 0.4990 nan 0.1000 -0.0005
320 0.4889 nan 0.1000 -0.0011
340 0.4773 nan 0.1000 -0.0015
360 0.4701 nan 0.1000 -0.0014
380 0.4584 nan 0.1000 -0.0016
400 0.4485 nan 0.1000 -0.0014
420 0.4396 nan 0.1000 -0.0009
440 0.4324 nan 0.1000 -0.0013
460 0.4269 nan 0.1000 -0.0009
480 0.4160 nan 0.1000 -0.0008
500 0.4077 nan 0.1000 -0.0011
520 0.4006 nan 0.1000 -0.0014
540 0.3933 nan 0.1000 -0.0007
560 0.3879 nan 0.1000 -0.0018
580 0.3812 nan 0.1000 -0.0017
600 0.3748 nan 0.1000 -0.0019
620 0.3685 nan 0.1000 -0.0010
640 0.3628 nan 0.1000 -0.0008
660 0.3571 nan 0.1000 -0.0015
680 0.3516 nan 0.1000 -0.0004
700 0.3458 nan 0.1000 -0.0009
720 0.3411 nan 0.1000 -0.0014
740 0.3366 nan 0.1000 -0.0018
760 0.3313 nan 0.1000 -0.0012
780 0.3274 nan 0.1000 -0.0005
800 0.3243 nan 0.1000 -0.0013
820 0.3191 nan 0.1000 -0.0009
840 0.3141 nan 0.1000 -0.0014
860 0.3114 nan 0.1000 -0.0014
880 0.3064 nan 0.1000 -0.0008
900 0.3027 nan 0.1000 -0.0004
920 0.2989 nan 0.1000 -0.0009
940 0.2943 nan 0.1000 -0.0007
960 0.2912 nan 0.1000 -0.0013
980 0.2882 nan 0.1000 -0.0012
1000 0.2828 nan 0.1000 -0.0007
1020 0.2793 nan 0.1000 -0.0009
1040 0.2767 nan 0.1000 -0.0010
1060 0.2723 nan 0.1000 -0.0009
1080 0.2688 nan 0.1000 -0.0006
1100 0.2648 nan 0.1000 -0.0007
- Fold10.Rep3: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3258 nan 0.0100 0.0028
2 1.3199 nan 0.0100 0.0027
3 1.3150 nan 0.0100 0.0027
4 1.3097 nan 0.0100 0.0026
5 1.3045 nan 0.0100 0.0025
6 1.2993 nan 0.0100 0.0026
7 1.2947 nan 0.0100 0.0025
8 1.2897 nan 0.0100 0.0024
9 1.2844 nan 0.0100 0.0023
10 1.2798 nan 0.0100 0.0024
20 1.2359 nan 0.0100 0.0019
40 1.1688 nan 0.0100 0.0013
60 1.1224 nan 0.0100 0.0008
80 1.0865 nan 0.0100 0.0007
100 1.0575 nan 0.0100 0.0006
120 1.0340 nan 0.0100 0.0005
140 1.0137 nan 0.0100 0.0004
160 0.9966 nan 0.0100 0.0002
180 0.9820 nan 0.0100 0.0003
200 0.9679 nan 0.0100 0.0003
220 0.9550 nan 0.0100 0.0002
240 0.9442 nan 0.0100 0.0002
260 0.9350 nan 0.0100 0.0002
280 0.9262 nan 0.0100 0.0002
300 0.9179 nan 0.0100 -0.0000
320 0.9110 nan 0.0100 0.0001
340 0.9043 nan 0.0100 0.0001
360 0.8978 nan 0.0100 0.0000
380 0.8921 nan 0.0100 0.0000
400 0.8872 nan 0.0100 -0.0000
420 0.8826 nan 0.0100 0.0000
440 0.8779 nan 0.0100 0.0000
460 0.8733 nan 0.0100 0.0000
480 0.8691 nan 0.0100 0.0001
500 0.8651 nan 0.0100 0.0000
520 0.8617 nan 0.0100 0.0000
540 0.8581 nan 0.0100 0.0001
560 0.8545 nan 0.0100 -0.0000
580 0.8512 nan 0.0100 0.0000
600 0.8480 nan 0.0100 0.0001
620 0.8451 nan 0.0100 0.0000
640 0.8421 nan 0.0100 -0.0001
660 0.8392 nan 0.0100 -0.0000
680 0.8366 nan 0.0100 0.0001
700 0.8341 nan 0.0100 0.0000
720 0.8319 nan 0.0100 -0.0000
740 0.8297 nan 0.0100 -0.0000
760 0.8272 nan 0.0100 0.0000
780 0.8249 nan 0.0100 0.0000
800 0.8228 nan 0.0100 -0.0000
820 0.8209 nan 0.0100 -0.0000
840 0.8188 nan 0.0100 -0.0000
860 0.8170 nan 0.0100 -0.0001
880 0.8152 nan 0.0100 -0.0000
900 0.8136 nan 0.0100 -0.0001
920 0.8118 nan 0.0100 -0.0000
940 0.8102 nan 0.0100 0.0000
960 0.8089 nan 0.0100 -0.0001
980 0.8074 nan 0.0100 -0.0001
1000 0.8059 nan 0.0100 -0.0001
1020 0.8045 nan 0.0100 -0.0000
1040 0.8032 nan 0.0100 -0.0001
1060 0.8016 nan 0.0100 -0.0000
1080 0.8002 nan 0.0100 -0.0000
1100 0.7988 nan 0.0100 -0.0001
- Fold01.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0033
2 1.3173 nan 0.0100 0.0037
3 1.3105 nan 0.0100 0.0032
4 1.3037 nan 0.0100 0.0032
5 1.2968 nan 0.0100 0.0033
6 1.2897 nan 0.0100 0.0032
7 1.2830 nan 0.0100 0.0033
8 1.2766 nan 0.0100 0.0032
9 1.2701 nan 0.0100 0.0029
10 1.2639 nan 0.0100 0.0028
20 1.2099 nan 0.0100 0.0025
40 1.1250 nan 0.0100 0.0018
60 1.0610 nan 0.0100 0.0013
80 1.0136 nan 0.0100 0.0010
100 0.9776 nan 0.0100 0.0006
120 0.9482 nan 0.0100 0.0005
140 0.9262 nan 0.0100 0.0003
160 0.9075 nan 0.0100 0.0003
180 0.8926 nan 0.0100 0.0001
200 0.8794 nan 0.0100 0.0001
220 0.8676 nan 0.0100 0.0001
240 0.8569 nan 0.0100 0.0001
260 0.8476 nan 0.0100 0.0001
280 0.8392 nan 0.0100 -0.0000
300 0.8316 nan 0.0100 -0.0001
320 0.8249 nan 0.0100 0.0000
340 0.8185 nan 0.0100 -0.0000
360 0.8125 nan 0.0100 0.0000
380 0.8071 nan 0.0100 0.0000
400 0.8024 nan 0.0100 0.0001
420 0.7982 nan 0.0100 -0.0000
440 0.7937 nan 0.0100 -0.0000
460 0.7889 nan 0.0100 -0.0000
480 0.7851 nan 0.0100 -0.0001
500 0.7813 nan 0.0100 -0.0000
520 0.7779 nan 0.0100 -0.0001
540 0.7748 nan 0.0100 -0.0000
560 0.7715 nan 0.0100 -0.0000
580 0.7682 nan 0.0100 -0.0002
600 0.7654 nan 0.0100 -0.0000
620 0.7628 nan 0.0100 -0.0001
640 0.7599 nan 0.0100 0.0000
660 0.7568 nan 0.0100 -0.0000
680 0.7545 nan 0.0100 -0.0000
700 0.7518 nan 0.0100 -0.0001
720 0.7492 nan 0.0100 -0.0001
740 0.7465 nan 0.0100 -0.0002
760 0.7442 nan 0.0100 -0.0001
780 0.7416 nan 0.0100 -0.0001
800 0.7391 nan 0.0100 -0.0001
820 0.7370 nan 0.0100 -0.0000
840 0.7348 nan 0.0100 -0.0001
860 0.7331 nan 0.0100 -0.0001
880 0.7310 nan 0.0100 -0.0001
900 0.7291 nan 0.0100 -0.0001
920 0.7275 nan 0.0100 -0.0000
940 0.7258 nan 0.0100 -0.0001
960 0.7238 nan 0.0100 -0.0001
980 0.7217 nan 0.0100 0.0000
1000 0.7198 nan 0.0100 -0.0001
1020 0.7180 nan 0.0100 -0.0000
1040 0.7159 nan 0.0100 -0.0002
1060 0.7144 nan 0.0100 -0.0001
1080 0.7126 nan 0.0100 -0.0000
1100 0.7110 nan 0.0100 -0.0001
- Fold01.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3230 nan 0.0100 0.0038
2 1.3155 nan 0.0100 0.0035
3 1.3078 nan 0.0100 0.0038
4 1.2999 nan 0.0100 0.0037
5 1.2927 nan 0.0100 0.0036
6 1.2861 nan 0.0100 0.0030
7 1.2789 nan 0.0100 0.0032
8 1.2714 nan 0.0100 0.0034
9 1.2648 nan 0.0100 0.0030
10 1.2582 nan 0.0100 0.0032
20 1.1971 nan 0.0100 0.0027
40 1.1041 nan 0.0100 0.0019
60 1.0354 nan 0.0100 0.0013
80 0.9834 nan 0.0100 0.0008
100 0.9435 nan 0.0100 0.0005
120 0.9145 nan 0.0100 0.0005
140 0.8912 nan 0.0100 0.0005
160 0.8725 nan 0.0100 0.0002
180 0.8560 nan 0.0100 0.0003
200 0.8423 nan 0.0100 0.0001
220 0.8304 nan 0.0100 0.0000
240 0.8192 nan 0.0100 0.0002
260 0.8088 nan 0.0100 0.0001
280 0.8000 nan 0.0100 -0.0000
300 0.7922 nan 0.0100 0.0001
320 0.7857 nan 0.0100 0.0001
340 0.7789 nan 0.0100 0.0000
360 0.7730 nan 0.0100 -0.0002
380 0.7673 nan 0.0100 -0.0001
400 0.7616 nan 0.0100 0.0001
420 0.7564 nan 0.0100 0.0000
440 0.7513 nan 0.0100 0.0000
460 0.7469 nan 0.0100 -0.0000
480 0.7426 nan 0.0100 -0.0000
500 0.7390 nan 0.0100 -0.0002
520 0.7351 nan 0.0100 -0.0001
540 0.7313 nan 0.0100 -0.0000
560 0.7277 nan 0.0100 0.0000
580 0.7234 nan 0.0100 -0.0002
600 0.7201 nan 0.0100 -0.0000
620 0.7167 nan 0.0100 -0.0002
640 0.7134 nan 0.0100 -0.0002
660 0.7103 nan 0.0100 -0.0001
680 0.7070 nan 0.0100 -0.0000
700 0.7038 nan 0.0100 -0.0000
720 0.7004 nan 0.0100 -0.0001
740 0.6973 nan 0.0100 -0.0001
760 0.6943 nan 0.0100 -0.0001
780 0.6911 nan 0.0100 -0.0001
800 0.6886 nan 0.0100 -0.0001
820 0.6860 nan 0.0100 -0.0001
840 0.6830 nan 0.0100 -0.0001
860 0.6807 nan 0.0100 -0.0001
880 0.6781 nan 0.0100 -0.0001
900 0.6755 nan 0.0100 -0.0001
920 0.6727 nan 0.0100 -0.0001
940 0.6704 nan 0.0100 -0.0001
960 0.6677 nan 0.0100 -0.0001
980 0.6655 nan 0.0100 -0.0001
1000 0.6629 nan 0.0100 -0.0000
1020 0.6601 nan 0.0100 -0.0000
1040 0.6578 nan 0.0100 -0.0002
1060 0.6557 nan 0.0100 -0.0002
1080 0.6535 nan 0.0100 -0.0002
1100 0.6514 nan 0.0100 -0.0001
- Fold01.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2761 nan 0.1000 0.0264
2 1.2333 nan 0.1000 0.0224
3 1.1978 nan 0.1000 0.0183
4 1.1618 nan 0.1000 0.0130
5 1.1361 nan 0.1000 0.0117
6 1.1191 nan 0.1000 0.0074
7 1.0972 nan 0.1000 0.0096
8 1.0806 nan 0.1000 0.0080
9 1.0670 nan 0.1000 0.0064
10 1.0518 nan 0.1000 0.0062
20 0.9638 nan 0.1000 0.0031
40 0.8833 nan 0.1000 0.0010
60 0.8459 nan 0.1000 0.0000
80 0.8210 nan 0.1000 -0.0001
100 0.8051 nan 0.1000 -0.0004
120 0.7931 nan 0.1000 -0.0008
140 0.7843 nan 0.1000 -0.0003
160 0.7747 nan 0.1000 -0.0002
180 0.7685 nan 0.1000 -0.0005
200 0.7620 nan 0.1000 -0.0007
220 0.7568 nan 0.1000 -0.0005
240 0.7528 nan 0.1000 -0.0007
260 0.7474 nan 0.1000 -0.0005
280 0.7448 nan 0.1000 -0.0019
300 0.7428 nan 0.1000 -0.0006
320 0.7374 nan 0.1000 -0.0000
340 0.7338 nan 0.1000 -0.0010
360 0.7312 nan 0.1000 -0.0005
380 0.7289 nan 0.1000 -0.0012
400 0.7254 nan 0.1000 -0.0002
420 0.7222 nan 0.1000 -0.0010
440 0.7179 nan 0.1000 -0.0004
460 0.7152 nan 0.1000 -0.0003
480 0.7118 nan 0.1000 -0.0006
500 0.7107 nan 0.1000 -0.0008
520 0.7099 nan 0.1000 -0.0005
540 0.7074 nan 0.1000 -0.0003
560 0.7060 nan 0.1000 -0.0012
580 0.7048 nan 0.1000 -0.0009
600 0.7026 nan 0.1000 -0.0007
620 0.7000 nan 0.1000 -0.0008
640 0.6985 nan 0.1000 -0.0008
660 0.6951 nan 0.1000 -0.0005
680 0.6931 nan 0.1000 -0.0005
700 0.6909 nan 0.1000 -0.0004
720 0.6885 nan 0.1000 -0.0006
740 0.6871 nan 0.1000 -0.0010
760 0.6862 nan 0.1000 -0.0004
780 0.6839 nan 0.1000 -0.0010
800 0.6834 nan 0.1000 -0.0009
820 0.6822 nan 0.1000 -0.0010
840 0.6813 nan 0.1000 -0.0010
860 0.6793 nan 0.1000 -0.0013
880 0.6781 nan 0.1000 -0.0007
900 0.6752 nan 0.1000 -0.0004
920 0.6732 nan 0.1000 -0.0004
940 0.6728 nan 0.1000 -0.0009
960 0.6712 nan 0.1000 -0.0005
980 0.6707 nan 0.1000 -0.0013
1000 0.6685 nan 0.1000 -0.0009
1020 0.6683 nan 0.1000 -0.0010
1040 0.6678 nan 0.1000 -0.0007
1060 0.6655 nan 0.1000 -0.0004
1080 0.6641 nan 0.1000 -0.0010
1100 0.6627 nan 0.1000 -0.0006
- Fold01.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2623 nan 0.1000 0.0361
2 1.2045 nan 0.1000 0.0272
3 1.1608 nan 0.1000 0.0219
4 1.1168 nan 0.1000 0.0195
5 1.0881 nan 0.1000 0.0142
6 1.0572 nan 0.1000 0.0153
7 1.0321 nan 0.1000 0.0113
8 1.0082 nan 0.1000 0.0087
9 0.9859 nan 0.1000 0.0089
10 0.9682 nan 0.1000 0.0064
20 0.8688 nan 0.1000 0.0007
40 0.8036 nan 0.1000 -0.0007
60 0.7648 nan 0.1000 -0.0001
80 0.7421 nan 0.1000 -0.0006
100 0.7195 nan 0.1000 -0.0019
120 0.6992 nan 0.1000 -0.0006
140 0.6849 nan 0.1000 -0.0006
160 0.6725 nan 0.1000 -0.0002
180 0.6592 nan 0.1000 -0.0005
200 0.6478 nan 0.1000 -0.0006
220 0.6376 nan 0.1000 -0.0005
240 0.6259 nan 0.1000 -0.0009
260 0.6183 nan 0.1000 -0.0006
280 0.6123 nan 0.1000 -0.0006
300 0.6024 nan 0.1000 -0.0006
320 0.5965 nan 0.1000 -0.0007
340 0.5881 nan 0.1000 -0.0008
360 0.5825 nan 0.1000 -0.0015
380 0.5759 nan 0.1000 -0.0010
400 0.5687 nan 0.1000 -0.0010
420 0.5622 nan 0.1000 -0.0010
440 0.5561 nan 0.1000 -0.0010
460 0.5501 nan 0.1000 -0.0012
480 0.5452 nan 0.1000 -0.0013
500 0.5398 nan 0.1000 -0.0007
520 0.5353 nan 0.1000 -0.0006
540 0.5314 nan 0.1000 -0.0012
560 0.5250 nan 0.1000 -0.0003
580 0.5168 nan 0.1000 -0.0008
600 0.5108 nan 0.1000 -0.0012
620 0.5053 nan 0.1000 -0.0009
640 0.4982 nan 0.1000 -0.0016
660 0.4941 nan 0.1000 -0.0009
680 0.4894 nan 0.1000 -0.0010
700 0.4840 nan 0.1000 -0.0011
720 0.4796 nan 0.1000 -0.0004
740 0.4767 nan 0.1000 -0.0007
760 0.4713 nan 0.1000 -0.0007
780 0.4690 nan 0.1000 -0.0009
800 0.4646 nan 0.1000 -0.0007
820 0.4595 nan 0.1000 -0.0008
840 0.4555 nan 0.1000 -0.0010
860 0.4512 nan 0.1000 -0.0010
880 0.4463 nan 0.1000 -0.0008
900 0.4424 nan 0.1000 -0.0005
920 0.4369 nan 0.1000 -0.0009
940 0.4337 nan 0.1000 -0.0003
960 0.4298 nan 0.1000 -0.0009
980 0.4249 nan 0.1000 -0.0009
1000 0.4214 nan 0.1000 -0.0007
1020 0.4187 nan 0.1000 -0.0011
1040 0.4163 nan 0.1000 -0.0009
1060 0.4147 nan 0.1000 -0.0009
1080 0.4117 nan 0.1000 -0.0010
1100 0.4080 nan 0.1000 -0.0003
- Fold01.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2529 nan 0.1000 0.0365
2 1.1944 nan 0.1000 0.0269
3 1.1455 nan 0.1000 0.0254
4 1.1054 nan 0.1000 0.0175
5 1.0676 nan 0.1000 0.0170
6 1.0322 nan 0.1000 0.0164
7 1.0028 nan 0.1000 0.0132
8 0.9790 nan 0.1000 0.0098
9 0.9578 nan 0.1000 0.0087
10 0.9412 nan 0.1000 0.0059
20 0.8436 nan 0.1000 0.0014
40 0.7668 nan 0.1000 0.0011
60 0.7249 nan 0.1000 -0.0022
80 0.6937 nan 0.1000 -0.0019
100 0.6636 nan 0.1000 -0.0006
120 0.6421 nan 0.1000 -0.0008
140 0.6163 nan 0.1000 -0.0013
160 0.5996 nan 0.1000 -0.0029
180 0.5836 nan 0.1000 -0.0010
200 0.5675 nan 0.1000 -0.0005
220 0.5530 nan 0.1000 -0.0020
240 0.5364 nan 0.1000 -0.0011
260 0.5249 nan 0.1000 -0.0030
280 0.5160 nan 0.1000 -0.0012
300 0.5023 nan 0.1000 -0.0015
320 0.4924 nan 0.1000 -0.0003
340 0.4823 nan 0.1000 -0.0012
360 0.4713 nan 0.1000 -0.0020
380 0.4611 nan 0.1000 -0.0002
400 0.4511 nan 0.1000 -0.0011
420 0.4442 nan 0.1000 -0.0015
440 0.4348 nan 0.1000 -0.0023
460 0.4262 nan 0.1000 -0.0013
480 0.4151 nan 0.1000 -0.0011
500 0.4093 nan 0.1000 -0.0013
520 0.4031 nan 0.1000 -0.0012
540 0.3978 nan 0.1000 -0.0008
560 0.3929 nan 0.1000 -0.0009
580 0.3848 nan 0.1000 -0.0012
600 0.3770 nan 0.1000 -0.0009
620 0.3719 nan 0.1000 -0.0021
640 0.3666 nan 0.1000 -0.0014
660 0.3609 nan 0.1000 -0.0005
680 0.3551 nan 0.1000 -0.0013
700 0.3502 nan 0.1000 -0.0008
720 0.3449 nan 0.1000 -0.0010
740 0.3394 nan 0.1000 -0.0006
760 0.3329 nan 0.1000 -0.0004
780 0.3285 nan 0.1000 -0.0005
800 0.3238 nan 0.1000 -0.0008
820 0.3188 nan 0.1000 -0.0010
840 0.3142 nan 0.1000 -0.0010
860 0.3105 nan 0.1000 -0.0009
880 0.3074 nan 0.1000 -0.0007
900 0.3030 nan 0.1000 -0.0011
920 0.2988 nan 0.1000 -0.0008
940 0.2955 nan 0.1000 -0.0006
960 0.2927 nan 0.1000 -0.0007
980 0.2880 nan 0.1000 -0.0003
1000 0.2843 nan 0.1000 -0.0013
1020 0.2815 nan 0.1000 -0.0004
1040 0.2784 nan 0.1000 -0.0006
1060 0.2731 nan 0.1000 -0.0007
1080 0.2702 nan 0.1000 -0.0010
1100 0.2672 nan 0.1000 -0.0004
- Fold01.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3266 nan 0.0100 0.0028
2 1.3213 nan 0.0100 0.0027
3 1.3154 nan 0.0100 0.0028
4 1.3102 nan 0.0100 0.0025
5 1.3047 nan 0.0100 0.0027
6 1.3004 nan 0.0100 0.0025
7 1.2947 nan 0.0100 0.0026
8 1.2899 nan 0.0100 0.0025
9 1.2848 nan 0.0100 0.0025
10 1.2801 nan 0.0100 0.0022
20 1.2344 nan 0.0100 0.0019
40 1.1660 nan 0.0100 0.0014
60 1.1209 nan 0.0100 0.0010
80 1.0862 nan 0.0100 0.0008
100 1.0577 nan 0.0100 0.0006
120 1.0338 nan 0.0100 0.0004
140 1.0133 nan 0.0100 0.0004
160 0.9954 nan 0.0100 0.0003
180 0.9808 nan 0.0100 0.0003
200 0.9677 nan 0.0100 0.0003
220 0.9560 nan 0.0100 0.0002
240 0.9457 nan 0.0100 0.0003
260 0.9362 nan 0.0100 0.0002
280 0.9278 nan 0.0100 0.0001
300 0.9200 nan 0.0100 0.0001
320 0.9126 nan 0.0100 0.0001
340 0.9059 nan 0.0100 0.0001
360 0.8998 nan 0.0100 0.0001
380 0.8940 nan 0.0100 0.0000
400 0.8890 nan 0.0100 0.0001
420 0.8843 nan 0.0100 -0.0000
440 0.8798 nan 0.0100 0.0000
460 0.8754 nan 0.0100 0.0000
480 0.8711 nan 0.0100 0.0000
500 0.8671 nan 0.0100 0.0001
520 0.8636 nan 0.0100 0.0000
540 0.8601 nan 0.0100 -0.0000
560 0.8570 nan 0.0100 0.0001
580 0.8540 nan 0.0100 0.0000
600 0.8508 nan 0.0100 0.0001
620 0.8480 nan 0.0100 0.0000
640 0.8451 nan 0.0100 -0.0000
660 0.8427 nan 0.0100 -0.0001
680 0.8401 nan 0.0100 -0.0001
700 0.8374 nan 0.0100 0.0000
720 0.8351 nan 0.0100 -0.0001
740 0.8329 nan 0.0100 -0.0001
760 0.8306 nan 0.0100 0.0000
780 0.8284 nan 0.0100 0.0000
800 0.8264 nan 0.0100 0.0000
820 0.8243 nan 0.0100 -0.0000
840 0.8226 nan 0.0100 0.0000
860 0.8208 nan 0.0100 -0.0001
880 0.8187 nan 0.0100 0.0000
900 0.8171 nan 0.0100 -0.0001
920 0.8156 nan 0.0100 -0.0000
940 0.8141 nan 0.0100 -0.0000
960 0.8126 nan 0.0100 -0.0001
980 0.8112 nan 0.0100 -0.0000
1000 0.8097 nan 0.0100 -0.0000
1020 0.8083 nan 0.0100 -0.0000
1040 0.8068 nan 0.0100 -0.0000
1060 0.8054 nan 0.0100 -0.0000
1080 0.8041 nan 0.0100 -0.0000
1100 0.8030 nan 0.0100 -0.0001
- Fold02.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3249 nan 0.0100 0.0033
2 1.3176 nan 0.0100 0.0034
3 1.3106 nan 0.0100 0.0033
4 1.3036 nan 0.0100 0.0034
5 1.2968 nan 0.0100 0.0033
6 1.2899 nan 0.0100 0.0033
7 1.2836 nan 0.0100 0.0031
8 1.2770 nan 0.0100 0.0030
9 1.2710 nan 0.0100 0.0031
10 1.2648 nan 0.0100 0.0030
20 1.2098 nan 0.0100 0.0023
40 1.1244 nan 0.0100 0.0017
60 1.0618 nan 0.0100 0.0011
80 1.0148 nan 0.0100 0.0007
100 0.9805 nan 0.0100 0.0007
120 0.9528 nan 0.0100 0.0005
140 0.9305 nan 0.0100 0.0004
160 0.9122 nan 0.0100 0.0002
180 0.8988 nan 0.0100 0.0000
200 0.8856 nan 0.0100 0.0002
220 0.8743 nan 0.0100 0.0002
240 0.8637 nan 0.0100 0.0001
260 0.8547 nan 0.0100 0.0001
280 0.8457 nan 0.0100 -0.0000
300 0.8379 nan 0.0100 0.0000
320 0.8303 nan 0.0100 0.0001
340 0.8241 nan 0.0100 0.0001
360 0.8181 nan 0.0100 -0.0000
380 0.8124 nan 0.0100 0.0002
400 0.8072 nan 0.0100 0.0001
420 0.8024 nan 0.0100 -0.0001
440 0.7973 nan 0.0100 0.0000
460 0.7930 nan 0.0100 0.0000
480 0.7887 nan 0.0100 -0.0000
500 0.7847 nan 0.0100 -0.0001
520 0.7811 nan 0.0100 -0.0001
540 0.7777 nan 0.0100 -0.0001
560 0.7738 nan 0.0100 0.0000
580 0.7703 nan 0.0100 -0.0000
600 0.7671 nan 0.0100 0.0001
620 0.7642 nan 0.0100 -0.0000
640 0.7606 nan 0.0100 0.0001
660 0.7576 nan 0.0100 -0.0001
680 0.7549 nan 0.0100 0.0000
700 0.7525 nan 0.0100 -0.0001
720 0.7498 nan 0.0100 0.0000
740 0.7474 nan 0.0100 -0.0001
760 0.7449 nan 0.0100 -0.0000
780 0.7425 nan 0.0100 -0.0001
800 0.7400 nan 0.0100 -0.0000
820 0.7380 nan 0.0100 -0.0002
840 0.7352 nan 0.0100 -0.0001
860 0.7329 nan 0.0100 -0.0001
880 0.7309 nan 0.0100 -0.0001
900 0.7287 nan 0.0100 -0.0001
920 0.7264 nan 0.0100 -0.0001
940 0.7241 nan 0.0100 -0.0001
960 0.7218 nan 0.0100 -0.0000
980 0.7202 nan 0.0100 -0.0001
1000 0.7183 nan 0.0100 -0.0001
1020 0.7164 nan 0.0100 -0.0001
1040 0.7148 nan 0.0100 -0.0000
1060 0.7127 nan 0.0100 -0.0001
1080 0.7109 nan 0.0100 -0.0001
1100 0.7087 nan 0.0100 -0.0000
- Fold02.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3240 nan 0.0100 0.0037
2 1.3161 nan 0.0100 0.0035
3 1.3082 nan 0.0100 0.0039
4 1.3017 nan 0.0100 0.0033
5 1.2946 nan 0.0100 0.0037
6 1.2871 nan 0.0100 0.0034
7 1.2805 nan 0.0100 0.0034
8 1.2735 nan 0.0100 0.0033
9 1.2670 nan 0.0100 0.0030
10 1.2599 nan 0.0100 0.0034
20 1.1993 nan 0.0100 0.0027
40 1.1043 nan 0.0100 0.0018
60 1.0358 nan 0.0100 0.0015
80 0.9863 nan 0.0100 0.0010
100 0.9491 nan 0.0100 0.0004
120 0.9191 nan 0.0100 0.0004
140 0.8950 nan 0.0100 0.0003
160 0.8755 nan 0.0100 0.0004
180 0.8594 nan 0.0100 0.0002
200 0.8454 nan 0.0100 0.0000
220 0.8332 nan 0.0100 0.0001
240 0.8222 nan 0.0100 0.0000
260 0.8123 nan 0.0100 0.0001
280 0.8033 nan 0.0100 0.0002
300 0.7954 nan 0.0100 -0.0001
320 0.7880 nan 0.0100 -0.0000
340 0.7811 nan 0.0100 -0.0000
360 0.7745 nan 0.0100 0.0000
380 0.7685 nan 0.0100 -0.0001
400 0.7631 nan 0.0100 -0.0001
420 0.7576 nan 0.0100 0.0000
440 0.7524 nan 0.0100 -0.0001
460 0.7473 nan 0.0100 -0.0002
480 0.7433 nan 0.0100 -0.0001
500 0.7398 nan 0.0100 -0.0001
520 0.7352 nan 0.0100 0.0000
540 0.7310 nan 0.0100 -0.0000
560 0.7269 nan 0.0100 -0.0000
580 0.7232 nan 0.0100 -0.0001
600 0.7187 nan 0.0100 -0.0002
620 0.7151 nan 0.0100 -0.0001
640 0.7114 nan 0.0100 -0.0000
660 0.7076 nan 0.0100 -0.0002
680 0.7043 nan 0.0100 -0.0000
700 0.7009 nan 0.0100 -0.0000
720 0.6979 nan 0.0100 -0.0002
740 0.6945 nan 0.0100 -0.0001
760 0.6912 nan 0.0100 -0.0001
780 0.6884 nan 0.0100 -0.0001
800 0.6852 nan 0.0100 -0.0001
820 0.6831 nan 0.0100 -0.0000
840 0.6805 nan 0.0100 -0.0001
860 0.6773 nan 0.0100 -0.0001
880 0.6746 nan 0.0100 -0.0001
900 0.6721 nan 0.0100 -0.0001
920 0.6695 nan 0.0100 -0.0002
940 0.6667 nan 0.0100 -0.0001
960 0.6643 nan 0.0100 -0.0002
980 0.6621 nan 0.0100 -0.0001
1000 0.6591 nan 0.0100 -0.0001
1020 0.6566 nan 0.0100 -0.0001
1040 0.6541 nan 0.0100 -0.0002
1060 0.6513 nan 0.0100 -0.0001
1080 0.6486 nan 0.0100 -0.0002
1100 0.6461 nan 0.0100 -0.0001
- Fold02.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2745 nan 0.1000 0.0280
2 1.2283 nan 0.1000 0.0224
3 1.1940 nan 0.1000 0.0179
4 1.1637 nan 0.1000 0.0152
5 1.1388 nan 0.1000 0.0123
6 1.1156 nan 0.1000 0.0101
7 1.0995 nan 0.1000 0.0047
8 1.0847 nan 0.1000 0.0071
9 1.0674 nan 0.1000 0.0080
10 1.0545 nan 0.1000 0.0063
20 0.9663 nan 0.1000 0.0024
40 0.8897 nan 0.1000 0.0007
60 0.8528 nan 0.1000 0.0005
80 0.8264 nan 0.1000 -0.0006
100 0.8099 nan 0.1000 -0.0000
120 0.7960 nan 0.1000 -0.0007
140 0.7860 nan 0.1000 -0.0003
160 0.7762 nan 0.1000 -0.0004
180 0.7705 nan 0.1000 -0.0007
200 0.7637 nan 0.1000 -0.0008
220 0.7568 nan 0.1000 -0.0019
240 0.7519 nan 0.1000 -0.0013
260 0.7474 nan 0.1000 -0.0010
280 0.7426 nan 0.1000 -0.0007
300 0.7391 nan 0.1000 -0.0004
320 0.7345 nan 0.1000 -0.0009
340 0.7327 nan 0.1000 -0.0009
360 0.7284 nan 0.1000 -0.0010
380 0.7249 nan 0.1000 -0.0006
400 0.7208 nan 0.1000 -0.0006
420 0.7169 nan 0.1000 -0.0013
440 0.7139 nan 0.1000 -0.0013
460 0.7114 nan 0.1000 -0.0005
480 0.7084 nan 0.1000 -0.0003
500 0.7067 nan 0.1000 -0.0009
520 0.7053 nan 0.1000 -0.0009
540 0.7025 nan 0.1000 -0.0007
560 0.7001 nan 0.1000 -0.0006
580 0.6974 nan 0.1000 -0.0007
600 0.6946 nan 0.1000 -0.0014
620 0.6930 nan 0.1000 -0.0009
640 0.6897 nan 0.1000 -0.0011
660 0.6876 nan 0.1000 -0.0013
680 0.6852 nan 0.1000 -0.0012
700 0.6838 nan 0.1000 -0.0011
720 0.6830 nan 0.1000 -0.0007
740 0.6812 nan 0.1000 -0.0007
760 0.6795 nan 0.1000 -0.0008
780 0.6777 nan 0.1000 -0.0011
800 0.6768 nan 0.1000 -0.0006
820 0.6750 nan 0.1000 -0.0003
840 0.6740 nan 0.1000 -0.0008
860 0.6733 nan 0.1000 -0.0005
880 0.6726 nan 0.1000 -0.0009
900 0.6705 nan 0.1000 -0.0018
920 0.6696 nan 0.1000 -0.0016
940 0.6679 nan 0.1000 -0.0008
960 0.6674 nan 0.1000 -0.0008
980 0.6663 nan 0.1000 -0.0009
1000 0.6664 nan 0.1000 -0.0011
1020 0.6639 nan 0.1000 -0.0008
1040 0.6631 nan 0.1000 -0.0007
1060 0.6625 nan 0.1000 -0.0012
1080 0.6610 nan 0.1000 -0.0011
1100 0.6592 nan 0.1000 -0.0009
- Fold02.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2637 nan 0.1000 0.0349
2 1.2098 nan 0.1000 0.0292
3 1.1585 nan 0.1000 0.0255
4 1.1184 nan 0.1000 0.0195
5 1.0850 nan 0.1000 0.0159
6 1.0561 nan 0.1000 0.0138
7 1.0298 nan 0.1000 0.0103
8 1.0064 nan 0.1000 0.0101
9 0.9875 nan 0.1000 0.0085
10 0.9704 nan 0.1000 0.0066
20 0.8762 nan 0.1000 0.0019
40 0.7991 nan 0.1000 -0.0005
60 0.7582 nan 0.1000 -0.0006
80 0.7369 nan 0.1000 -0.0008
100 0.7158 nan 0.1000 -0.0002
120 0.6947 nan 0.1000 -0.0003
140 0.6791 nan 0.1000 -0.0007
160 0.6693 nan 0.1000 -0.0011
180 0.6550 nan 0.1000 -0.0005
200 0.6418 nan 0.1000 -0.0006
220 0.6279 nan 0.1000 -0.0017
240 0.6163 nan 0.1000 -0.0018
260 0.6085 nan 0.1000 -0.0005
280 0.5996 nan 0.1000 -0.0012
300 0.5916 nan 0.1000 -0.0016
320 0.5836 nan 0.1000 -0.0017
340 0.5745 nan 0.1000 -0.0009
360 0.5657 nan 0.1000 -0.0004
380 0.5595 nan 0.1000 -0.0016
400 0.5501 nan 0.1000 -0.0013
420 0.5436 nan 0.1000 -0.0008
440 0.5368 nan 0.1000 -0.0008
460 0.5301 nan 0.1000 -0.0002
480 0.5240 nan 0.1000 -0.0011
500 0.5190 nan 0.1000 -0.0007
520 0.5125 nan 0.1000 -0.0006
540 0.5074 nan 0.1000 -0.0008
560 0.5021 nan 0.1000 -0.0004
580 0.4970 nan 0.1000 -0.0007
600 0.4916 nan 0.1000 -0.0003
620 0.4876 nan 0.1000 -0.0007
640 0.4824 nan 0.1000 -0.0008
660 0.4768 nan 0.1000 -0.0011
680 0.4735 nan 0.1000 -0.0011
700 0.4687 nan 0.1000 -0.0007
720 0.4642 nan 0.1000 -0.0017
740 0.4585 nan 0.1000 -0.0008
760 0.4542 nan 0.1000 -0.0010
780 0.4509 nan 0.1000 -0.0011
800 0.4466 nan 0.1000 -0.0005
820 0.4435 nan 0.1000 -0.0008
840 0.4398 nan 0.1000 -0.0006
860 0.4363 nan 0.1000 -0.0013
880 0.4331 nan 0.1000 -0.0006
900 0.4291 nan 0.1000 -0.0006
920 0.4260 nan 0.1000 -0.0005
940 0.4221 nan 0.1000 -0.0015
960 0.4166 nan 0.1000 -0.0010
980 0.4122 nan 0.1000 -0.0001
1000 0.4094 nan 0.1000 -0.0008
1020 0.4062 nan 0.1000 -0.0006
1040 0.4027 nan 0.1000 -0.0009
1060 0.3990 nan 0.1000 -0.0014
1080 0.3954 nan 0.1000 -0.0009
1100 0.3925 nan 0.1000 -0.0008
- Fold02.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2600 nan 0.1000 0.0403
2 1.1966 nan 0.1000 0.0295
3 1.1419 nan 0.1000 0.0257
4 1.0978 nan 0.1000 0.0216
5 1.0621 nan 0.1000 0.0168
6 1.0325 nan 0.1000 0.0136
7 1.0095 nan 0.1000 0.0108
8 0.9851 nan 0.1000 0.0119
9 0.9640 nan 0.1000 0.0093
10 0.9459 nan 0.1000 0.0076
20 0.8508 nan 0.1000 0.0024
40 0.7681 nan 0.1000 -0.0001
60 0.7195 nan 0.1000 -0.0005
80 0.6864 nan 0.1000 -0.0011
100 0.6627 nan 0.1000 -0.0010
120 0.6385 nan 0.1000 -0.0006
140 0.6172 nan 0.1000 -0.0014
160 0.5984 nan 0.1000 -0.0009
180 0.5790 nan 0.1000 -0.0012
200 0.5607 nan 0.1000 -0.0013
220 0.5486 nan 0.1000 -0.0011
240 0.5328 nan 0.1000 -0.0004
260 0.5165 nan 0.1000 -0.0012
280 0.5049 nan 0.1000 -0.0015
300 0.4945 nan 0.1000 -0.0016
320 0.4835 nan 0.1000 -0.0020
340 0.4755 nan 0.1000 -0.0013
360 0.4656 nan 0.1000 -0.0011
380 0.4555 nan 0.1000 -0.0009
400 0.4459 nan 0.1000 -0.0007
420 0.4393 nan 0.1000 -0.0008
440 0.4311 nan 0.1000 -0.0011
460 0.4224 nan 0.1000 -0.0010
480 0.4137 nan 0.1000 -0.0014
500 0.4081 nan 0.1000 -0.0009
520 0.4014 nan 0.1000 -0.0010
540 0.3928 nan 0.1000 -0.0019
560 0.3883 nan 0.1000 -0.0012
580 0.3807 nan 0.1000 -0.0008
600 0.3750 nan 0.1000 -0.0013
620 0.3685 nan 0.1000 -0.0008
640 0.3630 nan 0.1000 -0.0007
660 0.3580 nan 0.1000 -0.0007
680 0.3535 nan 0.1000 -0.0013
700 0.3477 nan 0.1000 -0.0006
720 0.3442 nan 0.1000 -0.0012
740 0.3366 nan 0.1000 -0.0008
760 0.3322 nan 0.1000 -0.0010
780 0.3260 nan 0.1000 -0.0005
800 0.3227 nan 0.1000 -0.0008
820 0.3195 nan 0.1000 -0.0010
840 0.3142 nan 0.1000 -0.0010
860 0.3092 nan 0.1000 -0.0009
880 0.3045 nan 0.1000 -0.0005
900 0.3002 nan 0.1000 -0.0005
920 0.2981 nan 0.1000 -0.0011
940 0.2934 nan 0.1000 -0.0013
960 0.2888 nan 0.1000 -0.0007
980 0.2844 nan 0.1000 -0.0006
1000 0.2803 nan 0.1000 -0.0008
1020 0.2775 nan 0.1000 -0.0010
1040 0.2745 nan 0.1000 -0.0010
1060 0.2707 nan 0.1000 -0.0012
1080 0.2674 nan 0.1000 -0.0008
1100 0.2643 nan 0.1000 -0.0009
- Fold02.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3257 nan 0.0100 0.0030
2 1.3199 nan 0.0100 0.0028
3 1.3139 nan 0.0100 0.0029
4 1.3083 nan 0.0100 0.0027
5 1.3032 nan 0.0100 0.0027
6 1.2972 nan 0.0100 0.0025
7 1.2916 nan 0.0100 0.0024
8 1.2861 nan 0.0100 0.0023
9 1.2811 nan 0.0100 0.0025
10 1.2764 nan 0.0100 0.0024
20 1.2320 nan 0.0100 0.0020
40 1.1657 nan 0.0100 0.0014
60 1.1192 nan 0.0100 0.0010
80 1.0830 nan 0.0100 0.0007
100 1.0520 nan 0.0100 0.0005
120 1.0268 nan 0.0100 0.0004
140 1.0058 nan 0.0100 0.0004
160 0.9881 nan 0.0100 0.0003
180 0.9723 nan 0.0100 0.0002
200 0.9587 nan 0.0100 0.0003
220 0.9474 nan 0.0100 0.0000
240 0.9368 nan 0.0100 0.0002
260 0.9275 nan 0.0100 0.0001
280 0.9193 nan 0.0100 -0.0000
300 0.9121 nan 0.0100 0.0001
320 0.9055 nan 0.0100 0.0001
340 0.8990 nan 0.0100 0.0001
360 0.8930 nan 0.0100 0.0001
380 0.8881 nan 0.0100 0.0000
400 0.8828 nan 0.0100 0.0001
420 0.8773 nan 0.0100 0.0001
440 0.8726 nan 0.0100 0.0000
460 0.8680 nan 0.0100 0.0000
480 0.8639 nan 0.0100 0.0001
500 0.8600 nan 0.0100 -0.0000
520 0.8565 nan 0.0100 -0.0001
540 0.8527 nan 0.0100 -0.0000
560 0.8493 nan 0.0100 -0.0000
580 0.8460 nan 0.0100 0.0000
600 0.8431 nan 0.0100 -0.0000
620 0.8403 nan 0.0100 0.0000
640 0.8376 nan 0.0100 -0.0000
660 0.8345 nan 0.0100 0.0000
680 0.8317 nan 0.0100 0.0000
700 0.8289 nan 0.0100 0.0000
720 0.8263 nan 0.0100 -0.0000
740 0.8238 nan 0.0100 0.0000
760 0.8216 nan 0.0100 0.0000
780 0.8195 nan 0.0100 -0.0000
800 0.8172 nan 0.0100 -0.0000
820 0.8155 nan 0.0100 -0.0001
840 0.8135 nan 0.0100 0.0000
860 0.8115 nan 0.0100 -0.0000
880 0.8097 nan 0.0100 -0.0001
900 0.8080 nan 0.0100 -0.0001
920 0.8064 nan 0.0100 -0.0000
940 0.8049 nan 0.0100 0.0000
960 0.8032 nan 0.0100 -0.0000
980 0.8017 nan 0.0100 -0.0000
1000 0.8002 nan 0.0100 -0.0000
1020 0.7987 nan 0.0100 -0.0001
1040 0.7971 nan 0.0100 -0.0000
1060 0.7956 nan 0.0100 -0.0000
1080 0.7943 nan 0.0100 -0.0000
1100 0.7928 nan 0.0100 0.0000
- Fold03.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3249 nan 0.0100 0.0036
2 1.3185 nan 0.0100 0.0033
3 1.3111 nan 0.0100 0.0034
4 1.3041 nan 0.0100 0.0034
5 1.2974 nan 0.0100 0.0033
6 1.2910 nan 0.0100 0.0033
7 1.2841 nan 0.0100 0.0032
8 1.2779 nan 0.0100 0.0033
9 1.2719 nan 0.0100 0.0029
10 1.2656 nan 0.0100 0.0032
20 1.2100 nan 0.0100 0.0025
40 1.1234 nan 0.0100 0.0018
60 1.0582 nan 0.0100 0.0012
80 1.0108 nan 0.0100 0.0009
100 0.9728 nan 0.0100 0.0007
120 0.9454 nan 0.0100 0.0005
140 0.9226 nan 0.0100 0.0004
160 0.9043 nan 0.0100 0.0004
180 0.8885 nan 0.0100 0.0003
200 0.8751 nan 0.0100 0.0003
220 0.8643 nan 0.0100 0.0001
240 0.8545 nan 0.0100 0.0000
260 0.8441 nan 0.0100 -0.0000
280 0.8363 nan 0.0100 -0.0000
300 0.8281 nan 0.0100 0.0002
320 0.8209 nan 0.0100 -0.0000
340 0.8144 nan 0.0100 0.0001
360 0.8084 nan 0.0100 0.0001
380 0.8030 nan 0.0100 -0.0000
400 0.7973 nan 0.0100 -0.0001
420 0.7926 nan 0.0100 -0.0001
440 0.7882 nan 0.0100 -0.0001
460 0.7837 nan 0.0100 -0.0001
480 0.7800 nan 0.0100 -0.0001
500 0.7760 nan 0.0100 -0.0001
520 0.7721 nan 0.0100 -0.0000
540 0.7689 nan 0.0100 -0.0000
560 0.7655 nan 0.0100 -0.0000
580 0.7623 nan 0.0100 -0.0001
600 0.7594 nan 0.0100 -0.0000
620 0.7561 nan 0.0100 0.0000
640 0.7530 nan 0.0100 -0.0000
660 0.7505 nan 0.0100 -0.0000
680 0.7482 nan 0.0100 0.0000
700 0.7459 nan 0.0100 -0.0002
720 0.7433 nan 0.0100 -0.0001
740 0.7404 nan 0.0100 -0.0001
760 0.7383 nan 0.0100 -0.0001
780 0.7358 nan 0.0100 -0.0000
800 0.7328 nan 0.0100 -0.0001
820 0.7307 nan 0.0100 -0.0001
840 0.7283 nan 0.0100 -0.0002
860 0.7260 nan 0.0100 0.0000
880 0.7236 nan 0.0100 -0.0000
900 0.7212 nan 0.0100 -0.0001
920 0.7187 nan 0.0100 -0.0000
940 0.7163 nan 0.0100 -0.0000
960 0.7142 nan 0.0100 -0.0001
980 0.7125 nan 0.0100 -0.0000
1000 0.7104 nan 0.0100 -0.0001
1020 0.7086 nan 0.0100 -0.0001
1040 0.7064 nan 0.0100 -0.0000
1060 0.7043 nan 0.0100 -0.0000
1080 0.7024 nan 0.0100 -0.0001
1100 0.7001 nan 0.0100 -0.0000
- Fold03.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0040
2 1.3163 nan 0.0100 0.0041
3 1.3080 nan 0.0100 0.0039
4 1.3005 nan 0.0100 0.0036
5 1.2936 nan 0.0100 0.0033
6 1.2861 nan 0.0100 0.0035
7 1.2789 nan 0.0100 0.0035
8 1.2721 nan 0.0100 0.0032
9 1.2651 nan 0.0100 0.0033
10 1.2585 nan 0.0100 0.0033
20 1.1981 nan 0.0100 0.0029
40 1.1036 nan 0.0100 0.0020
60 1.0329 nan 0.0100 0.0016
80 0.9813 nan 0.0100 0.0011
100 0.9412 nan 0.0100 0.0007
120 0.9096 nan 0.0100 0.0005
140 0.8865 nan 0.0100 0.0002
160 0.8671 nan 0.0100 0.0001
180 0.8498 nan 0.0100 0.0003
200 0.8363 nan 0.0100 0.0002
220 0.8241 nan 0.0100 -0.0001
240 0.8131 nan 0.0100 0.0000
260 0.8034 nan 0.0100 -0.0000
280 0.7946 nan 0.0100 0.0001
300 0.7871 nan 0.0100 -0.0001
320 0.7798 nan 0.0100 -0.0001
340 0.7734 nan 0.0100 0.0001
360 0.7672 nan 0.0100 0.0000
380 0.7606 nan 0.0100 -0.0001
400 0.7553 nan 0.0100 -0.0002
420 0.7495 nan 0.0100 -0.0000
440 0.7447 nan 0.0100 0.0001
460 0.7402 nan 0.0100 -0.0002
480 0.7352 nan 0.0100 -0.0000
500 0.7303 nan 0.0100 -0.0002
520 0.7266 nan 0.0100 -0.0000
540 0.7219 nan 0.0100 -0.0001
560 0.7184 nan 0.0100 -0.0000
580 0.7147 nan 0.0100 -0.0001
600 0.7107 nan 0.0100 -0.0000
620 0.7071 nan 0.0100 -0.0001
640 0.7037 nan 0.0100 -0.0001
660 0.7004 nan 0.0100 -0.0000
680 0.6970 nan 0.0100 -0.0000
700 0.6934 nan 0.0100 -0.0002
720 0.6899 nan 0.0100 -0.0000
740 0.6874 nan 0.0100 -0.0002
760 0.6839 nan 0.0100 -0.0002
780 0.6808 nan 0.0100 -0.0001
800 0.6773 nan 0.0100 -0.0001
820 0.6748 nan 0.0100 -0.0001
840 0.6720 nan 0.0100 -0.0001
860 0.6686 nan 0.0100 -0.0001
880 0.6658 nan 0.0100 -0.0001
900 0.6629 nan 0.0100 -0.0001
920 0.6603 nan 0.0100 0.0000
940 0.6577 nan 0.0100 -0.0001
960 0.6553 nan 0.0100 -0.0002
980 0.6526 nan 0.0100 -0.0000
1000 0.6495 nan 0.0100 -0.0001
1020 0.6470 nan 0.0100 -0.0001
1040 0.6448 nan 0.0100 -0.0001
1060 0.6422 nan 0.0100 -0.0002
1080 0.6396 nan 0.0100 0.0000
1100 0.6373 nan 0.0100 -0.0002
- Fold03.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2679 nan 0.1000 0.0253
2 1.2214 nan 0.1000 0.0214
3 1.1860 nan 0.1000 0.0177
4 1.1562 nan 0.1000 0.0141
5 1.1279 nan 0.1000 0.0117
6 1.1079 nan 0.1000 0.0088
7 1.0883 nan 0.1000 0.0085
8 1.0718 nan 0.1000 0.0064
9 1.0558 nan 0.1000 0.0076
10 1.0408 nan 0.1000 0.0061
20 0.9513 nan 0.1000 0.0018
40 0.8760 nan 0.1000 0.0005
60 0.8386 nan 0.1000 -0.0003
80 0.8166 nan 0.1000 0.0000
100 0.7994 nan 0.1000 -0.0004
120 0.7869 nan 0.1000 -0.0014
140 0.7756 nan 0.1000 -0.0006
160 0.7662 nan 0.1000 -0.0003
180 0.7580 nan 0.1000 -0.0011
200 0.7529 nan 0.1000 -0.0008
220 0.7467 nan 0.1000 -0.0012
240 0.7416 nan 0.1000 -0.0010
260 0.7348 nan 0.1000 -0.0003
280 0.7306 nan 0.1000 -0.0004
300 0.7271 nan 0.1000 -0.0005
320 0.7220 nan 0.1000 -0.0009
340 0.7168 nan 0.1000 -0.0009
360 0.7123 nan 0.1000 -0.0006
380 0.7083 nan 0.1000 -0.0007
400 0.7064 nan 0.1000 -0.0007
420 0.7024 nan 0.1000 -0.0002
440 0.6993 nan 0.1000 -0.0005
460 0.6964 nan 0.1000 -0.0015
480 0.6928 nan 0.1000 -0.0010
500 0.6911 nan 0.1000 -0.0001
520 0.6884 nan 0.1000 -0.0005
540 0.6860 nan 0.1000 -0.0010
560 0.6843 nan 0.1000 -0.0009
580 0.6827 nan 0.1000 -0.0011
600 0.6814 nan 0.1000 -0.0009
620 0.6794 nan 0.1000 -0.0003
640 0.6774 nan 0.1000 -0.0010
660 0.6749 nan 0.1000 -0.0008
680 0.6722 nan 0.1000 -0.0007
700 0.6702 nan 0.1000 -0.0004
720 0.6691 nan 0.1000 -0.0005
740 0.6665 nan 0.1000 -0.0014
760 0.6645 nan 0.1000 -0.0013
780 0.6631 nan 0.1000 -0.0012
800 0.6612 nan 0.1000 -0.0004
820 0.6593 nan 0.1000 -0.0005
840 0.6577 nan 0.1000 -0.0014
860 0.6561 nan 0.1000 -0.0005
880 0.6555 nan 0.1000 -0.0012
900 0.6546 nan 0.1000 -0.0006
920 0.6524 nan 0.1000 -0.0008
940 0.6501 nan 0.1000 -0.0008
960 0.6479 nan 0.1000 -0.0005
980 0.6461 nan 0.1000 -0.0007
1000 0.6458 nan 0.1000 -0.0010
1020 0.6447 nan 0.1000 -0.0005
1040 0.6433 nan 0.1000 -0.0007
1060 0.6416 nan 0.1000 -0.0006
1080 0.6395 nan 0.1000 -0.0009
1100 0.6383 nan 0.1000 -0.0007
- Fold03.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2657 nan 0.1000 0.0334
2 1.2089 nan 0.1000 0.0261
3 1.1568 nan 0.1000 0.0248
4 1.1152 nan 0.1000 0.0200
5 1.0793 nan 0.1000 0.0160
6 1.0506 nan 0.1000 0.0134
7 1.0244 nan 0.1000 0.0117
8 1.0018 nan 0.1000 0.0099
9 0.9843 nan 0.1000 0.0083
10 0.9665 nan 0.1000 0.0075
20 0.8720 nan 0.1000 0.0022
40 0.8014 nan 0.1000 -0.0006
60 0.7625 nan 0.1000 -0.0008
80 0.7343 nan 0.1000 -0.0008
100 0.7145 nan 0.1000 -0.0006
120 0.6982 nan 0.1000 -0.0007
140 0.6807 nan 0.1000 -0.0007
160 0.6644 nan 0.1000 -0.0022
180 0.6499 nan 0.1000 -0.0007
200 0.6322 nan 0.1000 -0.0006
220 0.6211 nan 0.1000 -0.0012
240 0.6119 nan 0.1000 -0.0011
260 0.6025 nan 0.1000 -0.0022
280 0.5923 nan 0.1000 -0.0007
300 0.5824 nan 0.1000 -0.0010
320 0.5731 nan 0.1000 -0.0012
340 0.5637 nan 0.1000 -0.0010
360 0.5576 nan 0.1000 -0.0010
380 0.5514 nan 0.1000 -0.0016
400 0.5428 nan 0.1000 -0.0016
420 0.5388 nan 0.1000 -0.0006
440 0.5338 nan 0.1000 -0.0014
460 0.5253 nan 0.1000 -0.0003
480 0.5200 nan 0.1000 -0.0009
500 0.5127 nan 0.1000 -0.0012
520 0.5078 nan 0.1000 -0.0009
540 0.5016 nan 0.1000 -0.0019
560 0.4968 nan 0.1000 -0.0009
580 0.4906 nan 0.1000 -0.0002
600 0.4843 nan 0.1000 -0.0008
620 0.4781 nan 0.1000 -0.0007
640 0.4727 nan 0.1000 -0.0008
660 0.4650 nan 0.1000 -0.0005
680 0.4607 nan 0.1000 -0.0007
700 0.4575 nan 0.1000 -0.0008
720 0.4524 nan 0.1000 -0.0004
740 0.4486 nan 0.1000 -0.0007
760 0.4445 nan 0.1000 -0.0009
780 0.4393 nan 0.1000 -0.0005
800 0.4334 nan 0.1000 -0.0011
820 0.4294 nan 0.1000 -0.0003
840 0.4272 nan 0.1000 -0.0015
860 0.4212 nan 0.1000 -0.0005
880 0.4166 nan 0.1000 -0.0001
900 0.4129 nan 0.1000 -0.0016
920 0.4097 nan 0.1000 -0.0001
940 0.4061 nan 0.1000 -0.0007
960 0.4023 nan 0.1000 -0.0004
980 0.3992 nan 0.1000 -0.0005
1000 0.3954 nan 0.1000 -0.0014
1020 0.3922 nan 0.1000 -0.0007
1040 0.3899 nan 0.1000 -0.0003
1060 0.3869 nan 0.1000 -0.0008
1080 0.3850 nan 0.1000 -0.0009
1100 0.3827 nan 0.1000 -0.0005
- Fold03.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2545 nan 0.1000 0.0374
2 1.1936 nan 0.1000 0.0287
3 1.1410 nan 0.1000 0.0267
4 1.0957 nan 0.1000 0.0220
5 1.0571 nan 0.1000 0.0172
6 1.0232 nan 0.1000 0.0151
7 0.9978 nan 0.1000 0.0120
8 0.9749 nan 0.1000 0.0103
9 0.9541 nan 0.1000 0.0091
10 0.9386 nan 0.1000 0.0057
20 0.8438 nan 0.1000 0.0014
40 0.7681 nan 0.1000 -0.0003
60 0.7254 nan 0.1000 -0.0015
80 0.6847 nan 0.1000 -0.0008
100 0.6630 nan 0.1000 -0.0017
120 0.6363 nan 0.1000 -0.0008
140 0.6136 nan 0.1000 -0.0016
160 0.5951 nan 0.1000 -0.0007
180 0.5754 nan 0.1000 -0.0004
200 0.5574 nan 0.1000 -0.0013
220 0.5428 nan 0.1000 -0.0004
240 0.5274 nan 0.1000 -0.0010
260 0.5095 nan 0.1000 -0.0012
280 0.4995 nan 0.1000 -0.0006
300 0.4880 nan 0.1000 -0.0011
320 0.4759 nan 0.1000 -0.0020
340 0.4652 nan 0.1000 -0.0014
360 0.4532 nan 0.1000 -0.0005
380 0.4425 nan 0.1000 -0.0018
400 0.4342 nan 0.1000 -0.0012
420 0.4249 nan 0.1000 -0.0010
440 0.4153 nan 0.1000 -0.0013
460 0.4107 nan 0.1000 -0.0019
480 0.4052 nan 0.1000 -0.0010
500 0.3979 nan 0.1000 -0.0011
520 0.3904 nan 0.1000 -0.0008
540 0.3842 nan 0.1000 -0.0012
560 0.3768 nan 0.1000 -0.0011
580 0.3699 nan 0.1000 -0.0011
600 0.3638 nan 0.1000 -0.0017
620 0.3582 nan 0.1000 -0.0009
640 0.3523 nan 0.1000 -0.0014
660 0.3462 nan 0.1000 -0.0001
680 0.3420 nan 0.1000 -0.0011
700 0.3359 nan 0.1000 -0.0004
720 0.3316 nan 0.1000 -0.0008
740 0.3266 nan 0.1000 -0.0010
760 0.3214 nan 0.1000 -0.0005
780 0.3163 nan 0.1000 -0.0018
800 0.3110 nan 0.1000 -0.0005
820 0.3068 nan 0.1000 -0.0005
840 0.3016 nan 0.1000 -0.0020
860 0.2972 nan 0.1000 -0.0010
880 0.2930 nan 0.1000 -0.0009
900 0.2889 nan 0.1000 -0.0005
920 0.2852 nan 0.1000 -0.0009
940 0.2815 nan 0.1000 -0.0017
960 0.2780 nan 0.1000 -0.0008
980 0.2745 nan 0.1000 -0.0004
1000 0.2707 nan 0.1000 -0.0007
1020 0.2667 nan 0.1000 -0.0008
1040 0.2643 nan 0.1000 -0.0018
1060 0.2618 nan 0.1000 -0.0007
1080 0.2579 nan 0.1000 -0.0006
1100 0.2550 nan 0.1000 -0.0004
- Fold03.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3252 nan 0.0100 0.0031
2 1.3195 nan 0.0100 0.0031
3 1.3132 nan 0.0100 0.0030
4 1.3075 nan 0.0100 0.0030
5 1.3019 nan 0.0100 0.0029
6 1.2959 nan 0.0100 0.0028
7 1.2902 nan 0.0100 0.0028
8 1.2844 nan 0.0100 0.0027
9 1.2795 nan 0.0100 0.0027
10 1.2742 nan 0.0100 0.0025
20 1.2254 nan 0.0100 0.0022
40 1.1533 nan 0.0100 0.0015
60 1.1019 nan 0.0100 0.0011
80 1.0638 nan 0.0100 0.0007
100 1.0347 nan 0.0100 0.0006
120 1.0104 nan 0.0100 0.0005
140 0.9891 nan 0.0100 0.0004
160 0.9716 nan 0.0100 0.0004
180 0.9567 nan 0.0100 0.0003
200 0.9429 nan 0.0100 0.0002
220 0.9311 nan 0.0100 0.0002
240 0.9199 nan 0.0100 0.0001
260 0.9102 nan 0.0100 0.0002
280 0.9025 nan 0.0100 0.0000
300 0.8948 nan 0.0100 0.0001
320 0.8882 nan 0.0100 0.0001
340 0.8817 nan 0.0100 0.0001
360 0.8758 nan 0.0100 0.0001
380 0.8704 nan 0.0100 0.0001
400 0.8654 nan 0.0100 0.0000
420 0.8606 nan 0.0100 0.0001
440 0.8559 nan 0.0100 0.0001
460 0.8518 nan 0.0100 0.0000
480 0.8477 nan 0.0100 -0.0000
500 0.8439 nan 0.0100 0.0000
520 0.8406 nan 0.0100 -0.0001
540 0.8370 nan 0.0100 0.0000
560 0.8335 nan 0.0100 0.0000
580 0.8303 nan 0.0100 0.0000
600 0.8271 nan 0.0100 0.0000
620 0.8244 nan 0.0100 0.0000
640 0.8216 nan 0.0100 -0.0000
660 0.8192 nan 0.0100 -0.0000
680 0.8165 nan 0.0100 -0.0001
700 0.8140 nan 0.0100 0.0000
720 0.8115 nan 0.0100 -0.0000
740 0.8093 nan 0.0100 -0.0001
760 0.8071 nan 0.0100 -0.0001
780 0.8050 nan 0.0100 -0.0000
800 0.8030 nan 0.0100 -0.0000
820 0.8008 nan 0.0100 -0.0000
840 0.7990 nan 0.0100 -0.0000
860 0.7971 nan 0.0100 -0.0000
880 0.7952 nan 0.0100 -0.0001
900 0.7934 nan 0.0100 0.0000
920 0.7918 nan 0.0100 0.0000
940 0.7900 nan 0.0100 -0.0000
960 0.7884 nan 0.0100 -0.0000
980 0.7868 nan 0.0100 -0.0000
1000 0.7851 nan 0.0100 0.0000
1020 0.7837 nan 0.0100 -0.0001
1040 0.7822 nan 0.0100 -0.0000
1060 0.7809 nan 0.0100 -0.0000
1080 0.7795 nan 0.0100 -0.0001
1100 0.7785 nan 0.0100 -0.0001
- Fold04.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0039
2 1.3163 nan 0.0100 0.0037
3 1.3085 nan 0.0100 0.0040
4 1.3013 nan 0.0100 0.0035
5 1.2942 nan 0.0100 0.0036
6 1.2869 nan 0.0100 0.0035
7 1.2801 nan 0.0100 0.0033
8 1.2735 nan 0.0100 0.0033
9 1.2674 nan 0.0100 0.0036
10 1.2606 nan 0.0100 0.0033
20 1.2010 nan 0.0100 0.0025
40 1.1097 nan 0.0100 0.0019
60 1.0438 nan 0.0100 0.0013
80 0.9936 nan 0.0100 0.0008
100 0.9569 nan 0.0100 0.0008
120 0.9280 nan 0.0100 0.0007
140 0.9043 nan 0.0100 0.0005
160 0.8866 nan 0.0100 0.0002
180 0.8720 nan 0.0100 0.0003
200 0.8590 nan 0.0100 0.0001
220 0.8478 nan 0.0100 0.0001
240 0.8377 nan 0.0100 0.0001
260 0.8284 nan 0.0100 0.0002
280 0.8205 nan 0.0100 -0.0000
300 0.8120 nan 0.0100 0.0001
320 0.8055 nan 0.0100 0.0001
340 0.7997 nan 0.0100 -0.0000
360 0.7941 nan 0.0100 -0.0001
380 0.7886 nan 0.0100 -0.0000
400 0.7839 nan 0.0100 0.0000
420 0.7791 nan 0.0100 -0.0001
440 0.7746 nan 0.0100 -0.0000
460 0.7700 nan 0.0100 0.0001
480 0.7662 nan 0.0100 -0.0001
500 0.7624 nan 0.0100 -0.0001
520 0.7588 nan 0.0100 -0.0001
540 0.7553 nan 0.0100 0.0000
560 0.7517 nan 0.0100 -0.0001
580 0.7485 nan 0.0100 -0.0001
600 0.7454 nan 0.0100 -0.0001
620 0.7423 nan 0.0100 -0.0001
640 0.7395 nan 0.0100 -0.0001
660 0.7368 nan 0.0100 -0.0000
680 0.7342 nan 0.0100 -0.0001
700 0.7319 nan 0.0100 -0.0002
720 0.7296 nan 0.0100 -0.0001
740 0.7266 nan 0.0100 -0.0001
760 0.7243 nan 0.0100 -0.0001
780 0.7222 nan 0.0100 -0.0001
800 0.7201 nan 0.0100 -0.0000
820 0.7179 nan 0.0100 -0.0001
840 0.7156 nan 0.0100 -0.0000
860 0.7135 nan 0.0100 -0.0002
880 0.7112 nan 0.0100 -0.0000
900 0.7092 nan 0.0100 -0.0001
920 0.7069 nan 0.0100 -0.0002
940 0.7052 nan 0.0100 -0.0001
960 0.7029 nan 0.0100 -0.0000
980 0.7011 nan 0.0100 -0.0002
1000 0.6995 nan 0.0100 -0.0001
1020 0.6975 nan 0.0100 -0.0001
1040 0.6956 nan 0.0100 -0.0001
1060 0.6938 nan 0.0100 -0.0001
1080 0.6917 nan 0.0100 -0.0001
1100 0.6897 nan 0.0100 -0.0001
- Fold04.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3233 nan 0.0100 0.0043
2 1.3148 nan 0.0100 0.0040
3 1.3068 nan 0.0100 0.0040
4 1.2989 nan 0.0100 0.0041
5 1.2913 nan 0.0100 0.0036
6 1.2838 nan 0.0100 0.0037
7 1.2758 nan 0.0100 0.0036
8 1.2687 nan 0.0100 0.0036
9 1.2618 nan 0.0100 0.0035
10 1.2548 nan 0.0100 0.0034
20 1.1919 nan 0.0100 0.0028
40 1.0919 nan 0.0100 0.0020
60 1.0198 nan 0.0100 0.0013
80 0.9651 nan 0.0100 0.0010
100 0.9237 nan 0.0100 0.0008
120 0.8931 nan 0.0100 0.0005
140 0.8695 nan 0.0100 0.0003
160 0.8513 nan 0.0100 0.0003
180 0.8341 nan 0.0100 0.0002
200 0.8196 nan 0.0100 -0.0001
220 0.8080 nan 0.0100 0.0002
240 0.7976 nan 0.0100 0.0001
260 0.7879 nan 0.0100 0.0000
280 0.7796 nan 0.0100 -0.0000
300 0.7719 nan 0.0100 -0.0000
320 0.7653 nan 0.0100 0.0000
340 0.7585 nan 0.0100 -0.0001
360 0.7532 nan 0.0100 -0.0001
380 0.7472 nan 0.0100 -0.0000
400 0.7416 nan 0.0100 0.0000
420 0.7359 nan 0.0100 -0.0000
440 0.7307 nan 0.0100 -0.0000
460 0.7265 nan 0.0100 -0.0000
480 0.7222 nan 0.0100 -0.0000
500 0.7180 nan 0.0100 -0.0000
520 0.7139 nan 0.0100 -0.0000
540 0.7102 nan 0.0100 -0.0000
560 0.7063 nan 0.0100 0.0000
580 0.7030 nan 0.0100 -0.0003
600 0.6993 nan 0.0100 -0.0001
620 0.6951 nan 0.0100 -0.0001
640 0.6919 nan 0.0100 -0.0001
660 0.6891 nan 0.0100 -0.0001
680 0.6858 nan 0.0100 -0.0000
700 0.6825 nan 0.0100 -0.0002
720 0.6792 nan 0.0100 -0.0001
740 0.6762 nan 0.0100 -0.0001
760 0.6732 nan 0.0100 -0.0001
780 0.6704 nan 0.0100 -0.0001
800 0.6676 nan 0.0100 -0.0001
820 0.6647 nan 0.0100 -0.0001
840 0.6619 nan 0.0100 -0.0001
860 0.6587 nan 0.0100 -0.0001
880 0.6558 nan 0.0100 -0.0000
900 0.6530 nan 0.0100 -0.0001
920 0.6506 nan 0.0100 -0.0001
940 0.6480 nan 0.0100 -0.0000
960 0.6456 nan 0.0100 -0.0001
980 0.6430 nan 0.0100 -0.0001
1000 0.6408 nan 0.0100 -0.0001
1020 0.6384 nan 0.0100 -0.0001
1040 0.6365 nan 0.0100 -0.0002
1060 0.6342 nan 0.0100 -0.0001
1080 0.6324 nan 0.0100 -0.0002
1100 0.6303 nan 0.0100 -0.0001
- Fold04.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2640 nan 0.1000 0.0289
2 1.2160 nan 0.1000 0.0233
3 1.1763 nan 0.1000 0.0187
4 1.1439 nan 0.1000 0.0161
5 1.1147 nan 0.1000 0.0131
6 1.0936 nan 0.1000 0.0109
7 1.0742 nan 0.1000 0.0085
8 1.0586 nan 0.1000 0.0078
9 1.0417 nan 0.1000 0.0077
10 1.0288 nan 0.1000 0.0058
20 0.9371 nan 0.1000 0.0022
40 0.8655 nan 0.1000 -0.0000
60 0.8273 nan 0.1000 0.0000
80 0.8013 nan 0.1000 -0.0009
100 0.7859 nan 0.1000 -0.0020
120 0.7729 nan 0.1000 -0.0001
140 0.7637 nan 0.1000 -0.0007
160 0.7531 nan 0.1000 -0.0003
180 0.7466 nan 0.1000 -0.0013
200 0.7404 nan 0.1000 -0.0006
220 0.7353 nan 0.1000 -0.0010
240 0.7301 nan 0.1000 -0.0007
260 0.7251 nan 0.1000 -0.0005
280 0.7204 nan 0.1000 -0.0005
300 0.7157 nan 0.1000 -0.0003
320 0.7126 nan 0.1000 -0.0008
340 0.7093 nan 0.1000 -0.0004
360 0.7054 nan 0.1000 -0.0010
380 0.7019 nan 0.1000 -0.0006
400 0.6991 nan 0.1000 -0.0018
420 0.6965 nan 0.1000 -0.0023
440 0.6931 nan 0.1000 -0.0006
460 0.6904 nan 0.1000 -0.0010
480 0.6882 nan 0.1000 -0.0005
500 0.6867 nan 0.1000 -0.0002
520 0.6842 nan 0.1000 -0.0007
540 0.6842 nan 0.1000 -0.0013
560 0.6819 nan 0.1000 -0.0006
580 0.6795 nan 0.1000 -0.0006
600 0.6783 nan 0.1000 -0.0006
620 0.6760 nan 0.1000 -0.0006
640 0.6746 nan 0.1000 -0.0011
660 0.6732 nan 0.1000 -0.0004
680 0.6717 nan 0.1000 -0.0006
700 0.6698 nan 0.1000 -0.0011
720 0.6672 nan 0.1000 -0.0007
740 0.6655 nan 0.1000 -0.0022
760 0.6642 nan 0.1000 -0.0008
780 0.6630 nan 0.1000 -0.0008
800 0.6602 nan 0.1000 -0.0002
820 0.6584 nan 0.1000 -0.0005
840 0.6561 nan 0.1000 -0.0010
860 0.6540 nan 0.1000 -0.0004
880 0.6525 nan 0.1000 -0.0013
900 0.6512 nan 0.1000 -0.0010
920 0.6498 nan 0.1000 -0.0014
940 0.6476 nan 0.1000 -0.0006
960 0.6461 nan 0.1000 -0.0004
980 0.6461 nan 0.1000 -0.0006
1000 0.6439 nan 0.1000 -0.0003
1020 0.6427 nan 0.1000 -0.0013
1040 0.6424 nan 0.1000 -0.0009
1060 0.6409 nan 0.1000 -0.0005
1080 0.6398 nan 0.1000 -0.0005
1100 0.6390 nan 0.1000 -0.0006
- Fold04.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2562 nan 0.1000 0.0363
2 1.1917 nan 0.1000 0.0297
3 1.1430 nan 0.1000 0.0213
4 1.1002 nan 0.1000 0.0207
5 1.0655 nan 0.1000 0.0177
6 1.0342 nan 0.1000 0.0158
7 1.0061 nan 0.1000 0.0118
8 0.9845 nan 0.1000 0.0090
9 0.9669 nan 0.1000 0.0095
10 0.9486 nan 0.1000 0.0085
20 0.8594 nan 0.1000 0.0016
40 0.7871 nan 0.1000 -0.0004
60 0.7487 nan 0.1000 -0.0016
80 0.7235 nan 0.1000 -0.0003
100 0.6983 nan 0.1000 0.0001
120 0.6827 nan 0.1000 -0.0014
140 0.6649 nan 0.1000 -0.0003
160 0.6532 nan 0.1000 -0.0004
180 0.6390 nan 0.1000 -0.0017
200 0.6278 nan 0.1000 -0.0003
220 0.6154 nan 0.1000 -0.0006
240 0.6062 nan 0.1000 -0.0009
260 0.5972 nan 0.1000 -0.0005
280 0.5884 nan 0.1000 -0.0008
300 0.5779 nan 0.1000 -0.0018
320 0.5680 nan 0.1000 -0.0005
340 0.5616 nan 0.1000 -0.0004
360 0.5532 nan 0.1000 -0.0014
380 0.5464 nan 0.1000 -0.0005
400 0.5405 nan 0.1000 -0.0009
420 0.5308 nan 0.1000 -0.0011
440 0.5255 nan 0.1000 -0.0008
460 0.5200 nan 0.1000 -0.0010
480 0.5131 nan 0.1000 -0.0006
500 0.5072 nan 0.1000 -0.0014
520 0.5024 nan 0.1000 -0.0008
540 0.4970 nan 0.1000 -0.0016
560 0.4919 nan 0.1000 -0.0015
580 0.4867 nan 0.1000 -0.0011
600 0.4817 nan 0.1000 -0.0006
620 0.4743 nan 0.1000 -0.0009
640 0.4700 nan 0.1000 -0.0011
660 0.4654 nan 0.1000 -0.0011
680 0.4609 nan 0.1000 -0.0008
700 0.4557 nan 0.1000 -0.0014
720 0.4502 nan 0.1000 -0.0015
740 0.4456 nan 0.1000 -0.0005
760 0.4417 nan 0.1000 -0.0013
780 0.4394 nan 0.1000 -0.0014
800 0.4351 nan 0.1000 -0.0015
820 0.4315 nan 0.1000 -0.0005
840 0.4263 nan 0.1000 -0.0006
860 0.4221 nan 0.1000 -0.0011
880 0.4200 nan 0.1000 -0.0009
900 0.4163 nan 0.1000 -0.0010
920 0.4119 nan 0.1000 -0.0014
940 0.4085 nan 0.1000 -0.0008
960 0.4047 nan 0.1000 -0.0006
980 0.4008 nan 0.1000 -0.0012
1000 0.3972 nan 0.1000 -0.0009
1020 0.3930 nan 0.1000 -0.0007
1040 0.3901 nan 0.1000 -0.0008
1060 0.3869 nan 0.1000 -0.0015
1080 0.3831 nan 0.1000 -0.0010
1100 0.3811 nan 0.1000 -0.0017
- Fold04.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2468 nan 0.1000 0.0400
2 1.1837 nan 0.1000 0.0310
3 1.1310 nan 0.1000 0.0257
4 1.0863 nan 0.1000 0.0212
5 1.0492 nan 0.1000 0.0157
6 1.0170 nan 0.1000 0.0148
7 0.9906 nan 0.1000 0.0099
8 0.9643 nan 0.1000 0.0112
9 0.9400 nan 0.1000 0.0113
10 0.9196 nan 0.1000 0.0075
20 0.8254 nan 0.1000 -0.0003
40 0.7445 nan 0.1000 -0.0018
60 0.7010 nan 0.1000 -0.0022
80 0.6707 nan 0.1000 -0.0007
100 0.6423 nan 0.1000 -0.0011
120 0.6178 nan 0.1000 -0.0011
140 0.5985 nan 0.1000 -0.0013
160 0.5793 nan 0.1000 -0.0012
180 0.5583 nan 0.1000 -0.0014
200 0.5414 nan 0.1000 -0.0011
220 0.5248 nan 0.1000 -0.0002
240 0.5109 nan 0.1000 -0.0007
260 0.5009 nan 0.1000 -0.0014
280 0.4882 nan 0.1000 -0.0008
300 0.4772 nan 0.1000 0.0001
320 0.4661 nan 0.1000 -0.0009
340 0.4578 nan 0.1000 -0.0012
360 0.4456 nan 0.1000 -0.0006
380 0.4406 nan 0.1000 -0.0009
400 0.4340 nan 0.1000 -0.0018
420 0.4225 nan 0.1000 -0.0010
440 0.4140 nan 0.1000 -0.0011
460 0.4067 nan 0.1000 -0.0013
480 0.3970 nan 0.1000 -0.0011
500 0.3891 nan 0.1000 -0.0010
520 0.3836 nan 0.1000 -0.0009
540 0.3770 nan 0.1000 -0.0010
560 0.3707 nan 0.1000 -0.0026
580 0.3644 nan 0.1000 -0.0014
600 0.3584 nan 0.1000 -0.0009
620 0.3522 nan 0.1000 -0.0008
640 0.3465 nan 0.1000 -0.0007
660 0.3398 nan 0.1000 -0.0013
680 0.3347 nan 0.1000 -0.0008
700 0.3287 nan 0.1000 -0.0010
720 0.3246 nan 0.1000 -0.0015
740 0.3195 nan 0.1000 -0.0009
760 0.3157 nan 0.1000 -0.0005
780 0.3110 nan 0.1000 -0.0007
800 0.3070 nan 0.1000 -0.0010
820 0.3007 nan 0.1000 -0.0007
840 0.2960 nan 0.1000 -0.0004
860 0.2931 nan 0.1000 -0.0006
880 0.2891 nan 0.1000 -0.0011
900 0.2852 nan 0.1000 -0.0010
920 0.2808 nan 0.1000 -0.0020
940 0.2779 nan 0.1000 -0.0012
960 0.2738 nan 0.1000 -0.0007
980 0.2714 nan 0.1000 -0.0007
1000 0.2681 nan 0.1000 -0.0007
1020 0.2657 nan 0.1000 -0.0007
1040 0.2613 nan 0.1000 -0.0009
1060 0.2582 nan 0.1000 -0.0008
1080 0.2546 nan 0.1000 -0.0004
1100 0.2520 nan 0.1000 -0.0009
- Fold04.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3261 nan 0.0100 0.0029
2 1.3205 nan 0.0100 0.0028
3 1.3146 nan 0.0100 0.0029
4 1.3098 nan 0.0100 0.0027
5 1.3049 nan 0.0100 0.0027
6 1.2997 nan 0.0100 0.0027
7 1.2943 nan 0.0100 0.0026
8 1.2890 nan 0.0100 0.0026
9 1.2834 nan 0.0100 0.0025
10 1.2785 nan 0.0100 0.0025
20 1.2341 nan 0.0100 0.0020
40 1.1633 nan 0.0100 0.0014
60 1.1157 nan 0.0100 0.0010
80 1.0793 nan 0.0100 0.0008
100 1.0501 nan 0.0100 0.0006
120 1.0253 nan 0.0100 0.0005
140 1.0054 nan 0.0100 0.0004
160 0.9887 nan 0.0100 0.0002
180 0.9742 nan 0.0100 0.0001
200 0.9614 nan 0.0100 0.0002
220 0.9498 nan 0.0100 0.0002
240 0.9397 nan 0.0100 0.0001
260 0.9307 nan 0.0100 0.0002
280 0.9221 nan 0.0100 0.0001
300 0.9154 nan 0.0100 0.0001
320 0.9088 nan 0.0100 0.0001
340 0.9019 nan 0.0100 0.0001
360 0.8965 nan 0.0100 0.0000
380 0.8912 nan 0.0100 -0.0000
400 0.8866 nan 0.0100 0.0000
420 0.8818 nan 0.0100 0.0000
440 0.8769 nan 0.0100 -0.0000
460 0.8727 nan 0.0100 -0.0000
480 0.8691 nan 0.0100 0.0000
500 0.8651 nan 0.0100 -0.0000
520 0.8615 nan 0.0100 0.0000
540 0.8583 nan 0.0100 0.0000
560 0.8551 nan 0.0100 0.0000
580 0.8519 nan 0.0100 -0.0000
600 0.8488 nan 0.0100 -0.0001
620 0.8458 nan 0.0100 -0.0000
640 0.8431 nan 0.0100 -0.0000
660 0.8403 nan 0.0100 -0.0000
680 0.8379 nan 0.0100 0.0000
700 0.8356 nan 0.0100 -0.0001
720 0.8329 nan 0.0100 -0.0001
740 0.8304 nan 0.0100 -0.0000
760 0.8281 nan 0.0100 0.0000
780 0.8259 nan 0.0100 -0.0001
800 0.8238 nan 0.0100 -0.0000
820 0.8216 nan 0.0100 -0.0001
840 0.8197 nan 0.0100 -0.0000
860 0.8178 nan 0.0100 0.0000
880 0.8158 nan 0.0100 0.0000
900 0.8143 nan 0.0100 -0.0001
920 0.8127 nan 0.0100 -0.0000
940 0.8111 nan 0.0100 -0.0001
960 0.8092 nan 0.0100 -0.0001
980 0.8077 nan 0.0100 -0.0001
1000 0.8060 nan 0.0100 -0.0000
1020 0.8046 nan 0.0100 -0.0001
1040 0.8034 nan 0.0100 -0.0001
1060 0.8020 nan 0.0100 -0.0001
1080 0.8008 nan 0.0100 -0.0001
1100 0.7993 nan 0.0100 -0.0001
- Fold05.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3247 nan 0.0100 0.0037
2 1.3176 nan 0.0100 0.0034
3 1.3104 nan 0.0100 0.0037
4 1.3032 nan 0.0100 0.0034
5 1.2966 nan 0.0100 0.0031
6 1.2906 nan 0.0100 0.0033
7 1.2839 nan 0.0100 0.0030
8 1.2773 nan 0.0100 0.0028
9 1.2704 nan 0.0100 0.0031
10 1.2644 nan 0.0100 0.0032
20 1.2089 nan 0.0100 0.0022
40 1.1211 nan 0.0100 0.0016
60 1.0571 nan 0.0100 0.0012
80 1.0099 nan 0.0100 0.0010
100 0.9753 nan 0.0100 0.0005
120 0.9473 nan 0.0100 0.0006
140 0.9246 nan 0.0100 0.0002
160 0.9071 nan 0.0100 0.0002
180 0.8923 nan 0.0100 0.0002
200 0.8791 nan 0.0100 0.0001
220 0.8679 nan 0.0100 0.0002
240 0.8581 nan 0.0100 0.0001
260 0.8486 nan 0.0100 0.0001
280 0.8411 nan 0.0100 0.0000
300 0.8336 nan 0.0100 0.0001
320 0.8260 nan 0.0100 -0.0000
340 0.8194 nan 0.0100 0.0000
360 0.8130 nan 0.0100 0.0001
380 0.8071 nan 0.0100 -0.0000
400 0.8022 nan 0.0100 0.0001
420 0.7972 nan 0.0100 0.0000
440 0.7917 nan 0.0100 0.0000
460 0.7874 nan 0.0100 0.0000
480 0.7834 nan 0.0100 -0.0001
500 0.7793 nan 0.0100 -0.0001
520 0.7753 nan 0.0100 0.0000
540 0.7718 nan 0.0100 -0.0000
560 0.7685 nan 0.0100 -0.0001
580 0.7652 nan 0.0100 -0.0001
600 0.7619 nan 0.0100 -0.0000
620 0.7588 nan 0.0100 -0.0001
640 0.7558 nan 0.0100 -0.0001
660 0.7529 nan 0.0100 -0.0001
680 0.7500 nan 0.0100 -0.0001
700 0.7471 nan 0.0100 -0.0001
720 0.7442 nan 0.0100 0.0000
740 0.7417 nan 0.0100 -0.0001
760 0.7391 nan 0.0100 -0.0001
780 0.7365 nan 0.0100 -0.0000
800 0.7341 nan 0.0100 -0.0001
820 0.7317 nan 0.0100 -0.0000
840 0.7291 nan 0.0100 -0.0001
860 0.7271 nan 0.0100 -0.0002
880 0.7251 nan 0.0100 -0.0000
900 0.7231 nan 0.0100 -0.0000
920 0.7209 nan 0.0100 -0.0001
940 0.7189 nan 0.0100 -0.0001
960 0.7164 nan 0.0100 -0.0000
980 0.7145 nan 0.0100 -0.0000
1000 0.7126 nan 0.0100 -0.0000
1020 0.7105 nan 0.0100 -0.0000
1040 0.7088 nan 0.0100 -0.0003
1060 0.7070 nan 0.0100 -0.0001
1080 0.7055 nan 0.0100 -0.0001
1100 0.7035 nan 0.0100 -0.0000
- Fold05.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0040
2 1.3167 nan 0.0100 0.0038
3 1.3087 nan 0.0100 0.0039
4 1.3013 nan 0.0100 0.0036
5 1.2940 nan 0.0100 0.0036
6 1.2867 nan 0.0100 0.0033
7 1.2798 nan 0.0100 0.0037
8 1.2732 nan 0.0100 0.0034
9 1.2666 nan 0.0100 0.0032
10 1.2596 nan 0.0100 0.0034
20 1.1968 nan 0.0100 0.0028
40 1.1006 nan 0.0100 0.0019
60 1.0316 nan 0.0100 0.0015
80 0.9786 nan 0.0100 0.0011
100 0.9396 nan 0.0100 0.0007
120 0.9098 nan 0.0100 0.0004
140 0.8870 nan 0.0100 0.0003
160 0.8672 nan 0.0100 0.0003
180 0.8512 nan 0.0100 0.0002
200 0.8376 nan 0.0100 0.0001
220 0.8251 nan 0.0100 0.0001
240 0.8146 nan 0.0100 0.0002
260 0.8044 nan 0.0100 0.0001
280 0.7958 nan 0.0100 0.0001
300 0.7871 nan 0.0100 -0.0000
320 0.7794 nan 0.0100 0.0001
340 0.7718 nan 0.0100 -0.0000
360 0.7658 nan 0.0100 -0.0000
380 0.7597 nan 0.0100 0.0000
400 0.7537 nan 0.0100 0.0000
420 0.7486 nan 0.0100 0.0000
440 0.7437 nan 0.0100 -0.0000
460 0.7391 nan 0.0100 -0.0001
480 0.7342 nan 0.0100 -0.0001
500 0.7295 nan 0.0100 -0.0000
520 0.7254 nan 0.0100 -0.0003
540 0.7214 nan 0.0100 0.0000
560 0.7179 nan 0.0100 -0.0000
580 0.7144 nan 0.0100 -0.0001
600 0.7110 nan 0.0100 -0.0001
620 0.7074 nan 0.0100 -0.0001
640 0.7041 nan 0.0100 -0.0001
660 0.7008 nan 0.0100 -0.0001
680 0.6968 nan 0.0100 -0.0001
700 0.6937 nan 0.0100 -0.0000
720 0.6902 nan 0.0100 -0.0002
740 0.6866 nan 0.0100 -0.0001
760 0.6837 nan 0.0100 -0.0001
780 0.6806 nan 0.0100 -0.0001
800 0.6775 nan 0.0100 -0.0001
820 0.6745 nan 0.0100 -0.0001
840 0.6716 nan 0.0100 -0.0001
860 0.6692 nan 0.0100 -0.0001
880 0.6666 nan 0.0100 -0.0001
900 0.6636 nan 0.0100 -0.0001
920 0.6606 nan 0.0100 -0.0002
940 0.6580 nan 0.0100 -0.0001
960 0.6557 nan 0.0100 -0.0003
980 0.6531 nan 0.0100 -0.0001
1000 0.6506 nan 0.0100 -0.0001
1020 0.6478 nan 0.0100 -0.0001
1040 0.6455 nan 0.0100 -0.0002
1060 0.6432 nan 0.0100 -0.0001
1080 0.6410 nan 0.0100 -0.0001
1100 0.6380 nan 0.0100 -0.0001
- Fold05.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2706 nan 0.1000 0.0283
2 1.2232 nan 0.1000 0.0225
3 1.1832 nan 0.1000 0.0173
4 1.1513 nan 0.1000 0.0146
5 1.1267 nan 0.1000 0.0123
6 1.1045 nan 0.1000 0.0093
7 1.0883 nan 0.1000 0.0081
8 1.0705 nan 0.1000 0.0077
9 1.0562 nan 0.1000 0.0062
10 1.0434 nan 0.1000 0.0068
20 0.9574 nan 0.1000 0.0025
40 0.8837 nan 0.1000 0.0003
60 0.8478 nan 0.1000 0.0006
80 0.8255 nan 0.1000 -0.0002
100 0.8062 nan 0.1000 0.0000
120 0.7943 nan 0.1000 -0.0010
140 0.7818 nan 0.1000 -0.0003
160 0.7731 nan 0.1000 -0.0003
180 0.7648 nan 0.1000 -0.0007
200 0.7578 nan 0.1000 -0.0006
220 0.7518 nan 0.1000 -0.0013
240 0.7475 nan 0.1000 -0.0004
260 0.7424 nan 0.1000 -0.0007
280 0.7377 nan 0.1000 0.0001
300 0.7358 nan 0.1000 0.0000
320 0.7327 nan 0.1000 -0.0010
340 0.7309 nan 0.1000 -0.0002
360 0.7277 nan 0.1000 -0.0008
380 0.7235 nan 0.1000 -0.0008
400 0.7209 nan 0.1000 -0.0008
420 0.7200 nan 0.1000 -0.0008
440 0.7156 nan 0.1000 -0.0009
460 0.7112 nan 0.1000 -0.0012
480 0.7080 nan 0.1000 -0.0003
500 0.7055 nan 0.1000 -0.0007
520 0.7042 nan 0.1000 -0.0007
540 0.7017 nan 0.1000 -0.0008
560 0.6983 nan 0.1000 -0.0008
580 0.6956 nan 0.1000 -0.0006
600 0.6941 nan 0.1000 -0.0005
620 0.6927 nan 0.1000 -0.0009
640 0.6907 nan 0.1000 -0.0006
660 0.6891 nan 0.1000 -0.0008
680 0.6867 nan 0.1000 -0.0011
700 0.6846 nan 0.1000 -0.0006
720 0.6830 nan 0.1000 -0.0011
740 0.6815 nan 0.1000 -0.0006
760 0.6794 nan 0.1000 -0.0017
780 0.6777 nan 0.1000 -0.0008
800 0.6769 nan 0.1000 -0.0008
820 0.6749 nan 0.1000 -0.0006
840 0.6731 nan 0.1000 -0.0008
860 0.6722 nan 0.1000 -0.0011
880 0.6705 nan 0.1000 -0.0009
900 0.6690 nan 0.1000 -0.0002
920 0.6673 nan 0.1000 -0.0007
940 0.6664 nan 0.1000 -0.0007
960 0.6649 nan 0.1000 -0.0007
980 0.6640 nan 0.1000 -0.0012
1000 0.6619 nan 0.1000 -0.0010
1020 0.6600 nan 0.1000 -0.0006
1040 0.6591 nan 0.1000 -0.0004
1060 0.6594 nan 0.1000 -0.0010
1080 0.6578 nan 0.1000 -0.0008
1100 0.6563 nan 0.1000 -0.0009
- Fold05.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2627 nan 0.1000 0.0362
2 1.2077 nan 0.1000 0.0275
3 1.1602 nan 0.1000 0.0228
4 1.1198 nan 0.1000 0.0204
5 1.0835 nan 0.1000 0.0167
6 1.0568 nan 0.1000 0.0138
7 1.0319 nan 0.1000 0.0114
8 1.0078 nan 0.1000 0.0107
9 0.9906 nan 0.1000 0.0055
10 0.9726 nan 0.1000 0.0029
20 0.8771 nan 0.1000 0.0011
40 0.8032 nan 0.1000 -0.0002
60 0.7614 nan 0.1000 0.0002
80 0.7342 nan 0.1000 -0.0005
100 0.7131 nan 0.1000 -0.0001
120 0.6946 nan 0.1000 -0.0020
140 0.6805 nan 0.1000 -0.0006
160 0.6704 nan 0.1000 -0.0015
180 0.6558 nan 0.1000 -0.0012
200 0.6426 nan 0.1000 -0.0016
220 0.6323 nan 0.1000 -0.0009
240 0.6205 nan 0.1000 -0.0005
260 0.6123 nan 0.1000 -0.0005
280 0.6047 nan 0.1000 -0.0004
300 0.5941 nan 0.1000 -0.0012
320 0.5864 nan 0.1000 -0.0003
340 0.5768 nan 0.1000 -0.0012
360 0.5686 nan 0.1000 -0.0005
380 0.5602 nan 0.1000 -0.0017
400 0.5514 nan 0.1000 -0.0008
420 0.5443 nan 0.1000 -0.0002
440 0.5359 nan 0.1000 -0.0004
460 0.5295 nan 0.1000 -0.0012
480 0.5245 nan 0.1000 -0.0018
500 0.5173 nan 0.1000 -0.0005
520 0.5128 nan 0.1000 -0.0008
540 0.5063 nan 0.1000 -0.0013
560 0.5007 nan 0.1000 -0.0006
580 0.4969 nan 0.1000 -0.0013
600 0.4899 nan 0.1000 -0.0002
620 0.4861 nan 0.1000 -0.0006
640 0.4805 nan 0.1000 -0.0006
660 0.4741 nan 0.1000 -0.0009
680 0.4693 nan 0.1000 -0.0005
700 0.4637 nan 0.1000 -0.0017
720 0.4597 nan 0.1000 -0.0007
740 0.4568 nan 0.1000 -0.0013
760 0.4506 nan 0.1000 -0.0009
780 0.4466 nan 0.1000 -0.0012
800 0.4431 nan 0.1000 -0.0012
820 0.4385 nan 0.1000 -0.0005
840 0.4339 nan 0.1000 -0.0004
860 0.4295 nan 0.1000 -0.0010
880 0.4256 nan 0.1000 -0.0001
900 0.4235 nan 0.1000 -0.0018
920 0.4185 nan 0.1000 -0.0010
940 0.4149 nan 0.1000 -0.0006
960 0.4120 nan 0.1000 -0.0009
980 0.4094 nan 0.1000 -0.0006
1000 0.4068 nan 0.1000 -0.0006
1020 0.4024 nan 0.1000 -0.0013
1040 0.3985 nan 0.1000 -0.0009
1060 0.3962 nan 0.1000 -0.0010
1080 0.3933 nan 0.1000 -0.0012
1100 0.3902 nan 0.1000 -0.0010
- Fold05.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2550 nan 0.1000 0.0373
2 1.1922 nan 0.1000 0.0319
3 1.1422 nan 0.1000 0.0263
4 1.0975 nan 0.1000 0.0204
5 1.0615 nan 0.1000 0.0177
6 1.0308 nan 0.1000 0.0151
7 1.0038 nan 0.1000 0.0134
8 0.9779 nan 0.1000 0.0121
9 0.9581 nan 0.1000 0.0083
10 0.9394 nan 0.1000 0.0094
20 0.8361 nan 0.1000 0.0023
40 0.7607 nan 0.1000 -0.0002
60 0.7165 nan 0.1000 -0.0015
80 0.6863 nan 0.1000 -0.0006
100 0.6624 nan 0.1000 -0.0021
120 0.6385 nan 0.1000 -0.0015
140 0.6170 nan 0.1000 -0.0024
160 0.5985 nan 0.1000 -0.0007
180 0.5820 nan 0.1000 -0.0005
200 0.5633 nan 0.1000 -0.0008
220 0.5507 nan 0.1000 -0.0020
240 0.5374 nan 0.1000 -0.0006
260 0.5252 nan 0.1000 -0.0007
280 0.5133 nan 0.1000 -0.0013
300 0.5001 nan 0.1000 -0.0021
320 0.4845 nan 0.1000 -0.0009
340 0.4756 nan 0.1000 -0.0009
360 0.4673 nan 0.1000 -0.0014
380 0.4566 nan 0.1000 -0.0008
400 0.4468 nan 0.1000 -0.0017
420 0.4377 nan 0.1000 -0.0006
440 0.4295 nan 0.1000 -0.0009
460 0.4209 nan 0.1000 -0.0005
480 0.4133 nan 0.1000 -0.0010
500 0.4025 nan 0.1000 -0.0005
520 0.3947 nan 0.1000 -0.0013
540 0.3874 nan 0.1000 -0.0011
560 0.3815 nan 0.1000 -0.0018
580 0.3745 nan 0.1000 -0.0007
600 0.3708 nan 0.1000 -0.0009
620 0.3641 nan 0.1000 -0.0019
640 0.3568 nan 0.1000 -0.0007
660 0.3522 nan 0.1000 -0.0005
680 0.3460 nan 0.1000 -0.0012
700 0.3410 nan 0.1000 -0.0008
720 0.3359 nan 0.1000 -0.0011
740 0.3307 nan 0.1000 -0.0006
760 0.3253 nan 0.1000 -0.0007
780 0.3205 nan 0.1000 -0.0008
800 0.3156 nan 0.1000 -0.0013
820 0.3123 nan 0.1000 -0.0007
840 0.3075 nan 0.1000 -0.0011
860 0.3024 nan 0.1000 -0.0005
880 0.2982 nan 0.1000 -0.0008
900 0.2950 nan 0.1000 -0.0013
920 0.2908 nan 0.1000 -0.0007
940 0.2863 nan 0.1000 -0.0006
960 0.2834 nan 0.1000 -0.0011
980 0.2799 nan 0.1000 -0.0013
1000 0.2773 nan 0.1000 -0.0005
1020 0.2739 nan 0.1000 -0.0008
1040 0.2701 nan 0.1000 -0.0008
1060 0.2663 nan 0.1000 -0.0005
1080 0.2633 nan 0.1000 -0.0003
1100 0.2613 nan 0.1000 -0.0009
- Fold05.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3257 nan 0.0100 0.0029
2 1.3195 nan 0.0100 0.0031
3 1.3132 nan 0.0100 0.0030
4 1.3072 nan 0.0100 0.0029
5 1.3015 nan 0.0100 0.0029
6 1.2954 nan 0.0100 0.0027
7 1.2902 nan 0.0100 0.0026
8 1.2852 nan 0.0100 0.0025
9 1.2800 nan 0.0100 0.0026
10 1.2744 nan 0.0100 0.0026
20 1.2292 nan 0.0100 0.0020
40 1.1564 nan 0.0100 0.0015
60 1.1069 nan 0.0100 0.0010
80 1.0701 nan 0.0100 0.0007
100 1.0397 nan 0.0100 0.0006
120 1.0151 nan 0.0100 0.0005
140 0.9949 nan 0.0100 0.0004
160 0.9767 nan 0.0100 0.0004
180 0.9604 nan 0.0100 0.0001
200 0.9469 nan 0.0100 0.0002
220 0.9343 nan 0.0100 0.0002
240 0.9233 nan 0.0100 0.0002
260 0.9133 nan 0.0100 0.0002
280 0.9049 nan 0.0100 0.0002
300 0.8970 nan 0.0100 0.0000
320 0.8895 nan 0.0100 0.0001
340 0.8829 nan 0.0100 0.0001
360 0.8770 nan 0.0100 0.0000
380 0.8711 nan 0.0100 0.0001
400 0.8656 nan 0.0100 0.0000
420 0.8608 nan 0.0100 0.0000
440 0.8561 nan 0.0100 -0.0000
460 0.8516 nan 0.0100 0.0000
480 0.8475 nan 0.0100 -0.0000
500 0.8435 nan 0.0100 -0.0000
520 0.8396 nan 0.0100 0.0000
540 0.8360 nan 0.0100 -0.0000
560 0.8325 nan 0.0100 0.0000
580 0.8289 nan 0.0100 0.0000
600 0.8255 nan 0.0100 0.0000
620 0.8225 nan 0.0100 0.0000
640 0.8196 nan 0.0100 -0.0001
660 0.8169 nan 0.0100 -0.0000
680 0.8142 nan 0.0100 0.0000
700 0.8117 nan 0.0100 -0.0001
720 0.8094 nan 0.0100 -0.0000
740 0.8071 nan 0.0100 -0.0000
760 0.8048 nan 0.0100 -0.0000
780 0.8023 nan 0.0100 -0.0000
800 0.8000 nan 0.0100 -0.0000
820 0.7979 nan 0.0100 -0.0000
840 0.7958 nan 0.0100 -0.0000
860 0.7938 nan 0.0100 -0.0000
880 0.7920 nan 0.0100 -0.0000
900 0.7902 nan 0.0100 -0.0000
920 0.7884 nan 0.0100 0.0000
940 0.7866 nan 0.0100 -0.0001
960 0.7848 nan 0.0100 -0.0001
980 0.7832 nan 0.0100 -0.0000
1000 0.7815 nan 0.0100 -0.0000
1020 0.7798 nan 0.0100 -0.0000
1040 0.7784 nan 0.0100 -0.0001
1060 0.7771 nan 0.0100 -0.0000
1080 0.7756 nan 0.0100 -0.0001
1100 0.7742 nan 0.0100 -0.0001
- Fold06.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3239 nan 0.0100 0.0037
2 1.3166 nan 0.0100 0.0038
3 1.3092 nan 0.0100 0.0036
4 1.3018 nan 0.0100 0.0036
5 1.2944 nan 0.0100 0.0037
6 1.2871 nan 0.0100 0.0035
7 1.2803 nan 0.0100 0.0032
8 1.2732 nan 0.0100 0.0033
9 1.2672 nan 0.0100 0.0029
10 1.2606 nan 0.0100 0.0033
20 1.2019 nan 0.0100 0.0027
40 1.1139 nan 0.0100 0.0017
60 1.0470 nan 0.0100 0.0013
80 0.9984 nan 0.0100 0.0010
100 0.9601 nan 0.0100 0.0008
120 0.9311 nan 0.0100 0.0006
140 0.9084 nan 0.0100 0.0004
160 0.8893 nan 0.0100 0.0002
180 0.8739 nan 0.0100 0.0002
200 0.8604 nan 0.0100 0.0002
220 0.8498 nan 0.0100 0.0002
240 0.8397 nan 0.0100 0.0002
260 0.8302 nan 0.0100 0.0001
280 0.8213 nan 0.0100 -0.0000
300 0.8131 nan 0.0100 0.0001
320 0.8056 nan 0.0100 0.0001
340 0.7992 nan 0.0100 0.0001
360 0.7932 nan 0.0100 0.0000
380 0.7875 nan 0.0100 0.0000
400 0.7816 nan 0.0100 0.0000
420 0.7762 nan 0.0100 0.0000
440 0.7717 nan 0.0100 -0.0000
460 0.7671 nan 0.0100 -0.0001
480 0.7627 nan 0.0100 -0.0000
500 0.7584 nan 0.0100 0.0000
520 0.7542 nan 0.0100 -0.0000
540 0.7506 nan 0.0100 -0.0000
560 0.7472 nan 0.0100 -0.0000
580 0.7439 nan 0.0100 -0.0001
600 0.7400 nan 0.0100 -0.0000
620 0.7372 nan 0.0100 -0.0000
640 0.7342 nan 0.0100 -0.0001
660 0.7308 nan 0.0100 -0.0000
680 0.7277 nan 0.0100 0.0000
700 0.7247 nan 0.0100 -0.0000
720 0.7217 nan 0.0100 -0.0001
740 0.7192 nan 0.0100 -0.0000
760 0.7163 nan 0.0100 -0.0000
780 0.7136 nan 0.0100 -0.0000
800 0.7111 nan 0.0100 -0.0001
820 0.7088 nan 0.0100 -0.0000
840 0.7063 nan 0.0100 -0.0002
860 0.7039 nan 0.0100 -0.0000
880 0.7016 nan 0.0100 0.0000
900 0.6996 nan 0.0100 -0.0000
920 0.6975 nan 0.0100 -0.0001
940 0.6950 nan 0.0100 -0.0000
960 0.6928 nan 0.0100 -0.0001
980 0.6909 nan 0.0100 -0.0001
1000 0.6888 nan 0.0100 -0.0000
1020 0.6866 nan 0.0100 -0.0001
1040 0.6847 nan 0.0100 -0.0000
1060 0.6829 nan 0.0100 -0.0001
1080 0.6812 nan 0.0100 -0.0001
1100 0.6793 nan 0.0100 -0.0001
- Fold06.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3237 nan 0.0100 0.0039
2 1.3154 nan 0.0100 0.0042
3 1.3072 nan 0.0100 0.0038
4 1.2986 nan 0.0100 0.0040
5 1.2908 nan 0.0100 0.0038
6 1.2837 nan 0.0100 0.0036
7 1.2758 nan 0.0100 0.0036
8 1.2684 nan 0.0100 0.0037
9 1.2615 nan 0.0100 0.0032
10 1.2551 nan 0.0100 0.0031
20 1.1916 nan 0.0100 0.0028
40 1.0904 nan 0.0100 0.0020
60 1.0186 nan 0.0100 0.0015
80 0.9660 nan 0.0100 0.0011
100 0.9263 nan 0.0100 0.0007
120 0.8949 nan 0.0100 0.0006
140 0.8712 nan 0.0100 0.0005
160 0.8516 nan 0.0100 0.0002
180 0.8350 nan 0.0100 0.0003
200 0.8206 nan 0.0100 0.0003
220 0.8083 nan 0.0100 -0.0000
240 0.7965 nan 0.0100 -0.0000
260 0.7863 nan 0.0100 0.0001
280 0.7771 nan 0.0100 0.0001
300 0.7685 nan 0.0100 0.0001
320 0.7610 nan 0.0100 -0.0000
340 0.7537 nan 0.0100 -0.0001
360 0.7474 nan 0.0100 -0.0000
380 0.7409 nan 0.0100 0.0000
400 0.7356 nan 0.0100 -0.0000
420 0.7302 nan 0.0100 -0.0002
440 0.7251 nan 0.0100 -0.0002
460 0.7201 nan 0.0100 -0.0001
480 0.7155 nan 0.0100 0.0000
500 0.7111 nan 0.0100 -0.0001
520 0.7068 nan 0.0100 -0.0002
540 0.7025 nan 0.0100 -0.0001
560 0.6979 nan 0.0100 -0.0001
580 0.6938 nan 0.0100 -0.0001
600 0.6899 nan 0.0100 -0.0001
620 0.6862 nan 0.0100 -0.0000
640 0.6823 nan 0.0100 -0.0001
660 0.6791 nan 0.0100 -0.0001
680 0.6759 nan 0.0100 -0.0001
700 0.6725 nan 0.0100 0.0000
720 0.6695 nan 0.0100 -0.0002
740 0.6664 nan 0.0100 -0.0001
760 0.6631 nan 0.0100 -0.0000
780 0.6601 nan 0.0100 -0.0001
800 0.6573 nan 0.0100 -0.0001
820 0.6545 nan 0.0100 -0.0001
840 0.6513 nan 0.0100 -0.0001
860 0.6486 nan 0.0100 -0.0001
880 0.6461 nan 0.0100 -0.0002
900 0.6435 nan 0.0100 -0.0001
920 0.6407 nan 0.0100 -0.0001
940 0.6378 nan 0.0100 -0.0001
960 0.6352 nan 0.0100 -0.0001
980 0.6324 nan 0.0100 -0.0002
1000 0.6296 nan 0.0100 -0.0001
1020 0.6271 nan 0.0100 -0.0001
1040 0.6244 nan 0.0100 -0.0000
1060 0.6217 nan 0.0100 -0.0001
1080 0.6194 nan 0.0100 -0.0001
1100 0.6166 nan 0.0100 -0.0001
- Fold06.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2770 nan 0.1000 0.0276
2 1.2292 nan 0.1000 0.0252
3 1.1888 nan 0.1000 0.0205
4 1.1499 nan 0.1000 0.0157
5 1.1229 nan 0.1000 0.0130
6 1.1034 nan 0.1000 0.0108
7 1.0833 nan 0.1000 0.0075
8 1.0651 nan 0.1000 0.0089
9 1.0510 nan 0.1000 0.0059
10 1.0403 nan 0.1000 0.0048
20 0.9458 nan 0.1000 0.0027
40 0.8663 nan 0.1000 -0.0003
60 0.8270 nan 0.1000 0.0007
80 0.7990 nan 0.1000 -0.0003
100 0.7795 nan 0.1000 -0.0002
120 0.7675 nan 0.1000 -0.0005
140 0.7562 nan 0.1000 -0.0004
160 0.7478 nan 0.1000 0.0000
180 0.7397 nan 0.1000 -0.0004
200 0.7343 nan 0.1000 -0.0004
220 0.7297 nan 0.1000 -0.0002
240 0.7239 nan 0.1000 -0.0008
260 0.7171 nan 0.1000 -0.0002
280 0.7124 nan 0.1000 -0.0002
300 0.7080 nan 0.1000 -0.0007
320 0.7029 nan 0.1000 -0.0003
340 0.6995 nan 0.1000 -0.0003
360 0.6971 nan 0.1000 -0.0007
380 0.6928 nan 0.1000 -0.0007
400 0.6891 nan 0.1000 -0.0008
420 0.6872 nan 0.1000 -0.0010
440 0.6840 nan 0.1000 -0.0002
460 0.6821 nan 0.1000 -0.0003
480 0.6784 nan 0.1000 -0.0006
500 0.6747 nan 0.1000 -0.0008
520 0.6712 nan 0.1000 -0.0012
540 0.6686 nan 0.1000 -0.0011
560 0.6673 nan 0.1000 -0.0008
580 0.6657 nan 0.1000 -0.0004
600 0.6630 nan 0.1000 -0.0008
620 0.6605 nan 0.1000 -0.0003
640 0.6586 nan 0.1000 -0.0006
660 0.6575 nan 0.1000 -0.0014
680 0.6565 nan 0.1000 -0.0005
700 0.6544 nan 0.1000 -0.0008
720 0.6519 nan 0.1000 -0.0009
740 0.6512 nan 0.1000 -0.0010
760 0.6486 nan 0.1000 -0.0004
780 0.6483 nan 0.1000 -0.0017
800 0.6468 nan 0.1000 -0.0010
820 0.6441 nan 0.1000 -0.0006
840 0.6416 nan 0.1000 -0.0008
860 0.6384 nan 0.1000 -0.0003
880 0.6379 nan 0.1000 -0.0006
900 0.6346 nan 0.1000 -0.0005
920 0.6337 nan 0.1000 -0.0010
940 0.6319 nan 0.1000 -0.0006
960 0.6298 nan 0.1000 -0.0011
980 0.6280 nan 0.1000 -0.0005
1000 0.6264 nan 0.1000 -0.0007
1020 0.6249 nan 0.1000 -0.0002
1040 0.6224 nan 0.1000 -0.0007
1060 0.6214 nan 0.1000 -0.0007
1080 0.6200 nan 0.1000 -0.0008
1100 0.6179 nan 0.1000 -0.0005
- Fold06.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2547 nan 0.1000 0.0355
2 1.1966 nan 0.1000 0.0294
3 1.1518 nan 0.1000 0.0200
4 1.1099 nan 0.1000 0.0221
5 1.0726 nan 0.1000 0.0177
6 1.0414 nan 0.1000 0.0139
7 1.0163 nan 0.1000 0.0115
8 0.9900 nan 0.1000 0.0126
9 0.9710 nan 0.1000 0.0093
10 0.9522 nan 0.1000 0.0084
20 0.8534 nan 0.1000 0.0013
40 0.7749 nan 0.1000 0.0002
60 0.7327 nan 0.1000 -0.0000
80 0.7023 nan 0.1000 0.0006
100 0.6842 nan 0.1000 -0.0009
120 0.6673 nan 0.1000 -0.0006
140 0.6534 nan 0.1000 -0.0012
160 0.6419 nan 0.1000 -0.0017
180 0.6300 nan 0.1000 -0.0007
200 0.6203 nan 0.1000 -0.0006
220 0.6098 nan 0.1000 -0.0011
240 0.5977 nan 0.1000 0.0002
260 0.5880 nan 0.1000 -0.0001
280 0.5800 nan 0.1000 -0.0013
300 0.5722 nan 0.1000 -0.0010
320 0.5627 nan 0.1000 -0.0005
340 0.5534 nan 0.1000 -0.0011
360 0.5436 nan 0.1000 -0.0019
380 0.5359 nan 0.1000 -0.0007
400 0.5280 nan 0.1000 -0.0014
420 0.5193 nan 0.1000 -0.0004
440 0.5141 nan 0.1000 -0.0005
460 0.5071 nan 0.1000 -0.0006
480 0.4997 nan 0.1000 -0.0017
500 0.4945 nan 0.1000 -0.0008
520 0.4887 nan 0.1000 -0.0015
540 0.4822 nan 0.1000 -0.0008
560 0.4764 nan 0.1000 -0.0010
580 0.4713 nan 0.1000 -0.0013
600 0.4664 nan 0.1000 -0.0008
620 0.4619 nan 0.1000 -0.0015
640 0.4562 nan 0.1000 -0.0012
660 0.4515 nan 0.1000 -0.0009
680 0.4460 nan 0.1000 -0.0006
700 0.4430 nan 0.1000 -0.0008
720 0.4398 nan 0.1000 -0.0010
740 0.4343 nan 0.1000 -0.0016
760 0.4314 nan 0.1000 -0.0010
780 0.4263 nan 0.1000 -0.0006
800 0.4229 nan 0.1000 -0.0009
820 0.4194 nan 0.1000 -0.0008
840 0.4167 nan 0.1000 -0.0008
860 0.4130 nan 0.1000 -0.0009
880 0.4092 nan 0.1000 -0.0011
900 0.4060 nan 0.1000 -0.0005
920 0.4027 nan 0.1000 -0.0007
940 0.3989 nan 0.1000 -0.0014
960 0.3957 nan 0.1000 -0.0010
980 0.3904 nan 0.1000 -0.0001
1000 0.3880 nan 0.1000 -0.0010
1020 0.3854 nan 0.1000 -0.0013
1040 0.3816 nan 0.1000 -0.0005
1060 0.3787 nan 0.1000 -0.0003
1080 0.3762 nan 0.1000 -0.0008
1100 0.3734 nan 0.1000 -0.0009
- Fold06.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2528 nan 0.1000 0.0376
2 1.1836 nan 0.1000 0.0329
3 1.1320 nan 0.1000 0.0221
4 1.0875 nan 0.1000 0.0222
5 1.0464 nan 0.1000 0.0195
6 1.0147 nan 0.1000 0.0170
7 0.9852 nan 0.1000 0.0115
8 0.9576 nan 0.1000 0.0120
9 0.9353 nan 0.1000 0.0099
10 0.9137 nan 0.1000 0.0091
20 0.8108 nan 0.1000 0.0024
40 0.7295 nan 0.1000 -0.0000
60 0.6854 nan 0.1000 -0.0006
80 0.6540 nan 0.1000 -0.0025
100 0.6304 nan 0.1000 -0.0013
120 0.6022 nan 0.1000 -0.0005
140 0.5798 nan 0.1000 -0.0014
160 0.5631 nan 0.1000 -0.0009
180 0.5460 nan 0.1000 -0.0012
200 0.5294 nan 0.1000 0.0001
220 0.5145 nan 0.1000 -0.0013
240 0.5015 nan 0.1000 -0.0005
260 0.4908 nan 0.1000 -0.0021
280 0.4812 nan 0.1000 -0.0024
300 0.4712 nan 0.1000 -0.0012
320 0.4586 nan 0.1000 -0.0006
340 0.4492 nan 0.1000 -0.0006
360 0.4390 nan 0.1000 -0.0013
380 0.4292 nan 0.1000 -0.0013
400 0.4215 nan 0.1000 -0.0005
420 0.4154 nan 0.1000 -0.0009
440 0.4073 nan 0.1000 -0.0012
460 0.3994 nan 0.1000 -0.0012
480 0.3916 nan 0.1000 -0.0013
500 0.3844 nan 0.1000 -0.0011
520 0.3765 nan 0.1000 -0.0014
540 0.3692 nan 0.1000 -0.0009
560 0.3635 nan 0.1000 -0.0010
580 0.3557 nan 0.1000 -0.0014
600 0.3514 nan 0.1000 -0.0012
620 0.3443 nan 0.1000 -0.0010
640 0.3396 nan 0.1000 -0.0008
660 0.3345 nan 0.1000 -0.0010
680 0.3303 nan 0.1000 -0.0011
700 0.3256 nan 0.1000 -0.0015
720 0.3185 nan 0.1000 -0.0009
740 0.3126 nan 0.1000 -0.0009
760 0.3075 nan 0.1000 -0.0007
780 0.3037 nan 0.1000 -0.0009
800 0.2979 nan 0.1000 -0.0004
820 0.2934 nan 0.1000 -0.0011
840 0.2900 nan 0.1000 -0.0015
860 0.2859 nan 0.1000 -0.0013
880 0.2815 nan 0.1000 -0.0010
900 0.2779 nan 0.1000 -0.0012
920 0.2726 nan 0.1000 -0.0005
940 0.2691 nan 0.1000 -0.0006
960 0.2655 nan 0.1000 -0.0008
980 0.2635 nan 0.1000 -0.0006
1000 0.2598 nan 0.1000 -0.0007
1020 0.2560 nan 0.1000 -0.0007
1040 0.2517 nan 0.1000 -0.0007
1060 0.2486 nan 0.1000 -0.0006
1080 0.2453 nan 0.1000 -0.0006
1100 0.2428 nan 0.1000 -0.0011
- Fold06.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3253 nan 0.0100 0.0031
2 1.3188 nan 0.0100 0.0031
3 1.3128 nan 0.0100 0.0029
4 1.3071 nan 0.0100 0.0030
5 1.3011 nan 0.0100 0.0030
6 1.2952 nan 0.0100 0.0028
7 1.2891 nan 0.0100 0.0028
8 1.2836 nan 0.0100 0.0025
9 1.2785 nan 0.0100 0.0026
10 1.2734 nan 0.0100 0.0027
20 1.2247 nan 0.0100 0.0022
40 1.1508 nan 0.0100 0.0015
60 1.1004 nan 0.0100 0.0010
80 1.0625 nan 0.0100 0.0008
100 1.0323 nan 0.0100 0.0006
120 1.0061 nan 0.0100 0.0006
140 0.9845 nan 0.0100 0.0004
160 0.9657 nan 0.0100 0.0002
180 0.9499 nan 0.0100 0.0003
200 0.9359 nan 0.0100 0.0003
220 0.9239 nan 0.0100 0.0002
240 0.9137 nan 0.0100 0.0001
260 0.9042 nan 0.0100 0.0002
280 0.8952 nan 0.0100 0.0000
300 0.8880 nan 0.0100 0.0001
320 0.8809 nan 0.0100 0.0001
340 0.8747 nan 0.0100 0.0000
360 0.8687 nan 0.0100 0.0000
380 0.8637 nan 0.0100 -0.0000
400 0.8582 nan 0.0100 -0.0000
420 0.8533 nan 0.0100 0.0000
440 0.8491 nan 0.0100 0.0000
460 0.8452 nan 0.0100 0.0001
480 0.8415 nan 0.0100 0.0000
500 0.8376 nan 0.0100 0.0001
520 0.8344 nan 0.0100 -0.0000
540 0.8309 nan 0.0100 0.0000
560 0.8277 nan 0.0100 0.0000
580 0.8245 nan 0.0100 -0.0001
600 0.8215 nan 0.0100 -0.0001
620 0.8189 nan 0.0100 -0.0000
640 0.8163 nan 0.0100 -0.0000
660 0.8139 nan 0.0100 -0.0000
680 0.8114 nan 0.0100 0.0000
700 0.8092 nan 0.0100 -0.0001
720 0.8071 nan 0.0100 -0.0001
740 0.8049 nan 0.0100 -0.0000
760 0.8030 nan 0.0100 -0.0001
780 0.8012 nan 0.0100 0.0000
800 0.7994 nan 0.0100 -0.0001
820 0.7978 nan 0.0100 -0.0000
840 0.7962 nan 0.0100 -0.0001
860 0.7943 nan 0.0100 0.0000
880 0.7927 nan 0.0100 -0.0000
900 0.7911 nan 0.0100 -0.0001
920 0.7895 nan 0.0100 -0.0000
940 0.7880 nan 0.0100 -0.0000
960 0.7864 nan 0.0100 -0.0000
980 0.7848 nan 0.0100 -0.0000
1000 0.7833 nan 0.0100 -0.0000
1020 0.7819 nan 0.0100 -0.0000
1040 0.7806 nan 0.0100 -0.0000
1060 0.7794 nan 0.0100 -0.0000
1080 0.7779 nan 0.0100 -0.0000
1100 0.7766 nan 0.0100 -0.0000
- Fold07.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3236 nan 0.0100 0.0040
2 1.3158 nan 0.0100 0.0041
3 1.3085 nan 0.0100 0.0035
4 1.3015 nan 0.0100 0.0033
5 1.2939 nan 0.0100 0.0036
6 1.2866 nan 0.0100 0.0035
7 1.2796 nan 0.0100 0.0033
8 1.2728 nan 0.0100 0.0033
9 1.2657 nan 0.0100 0.0034
10 1.2587 nan 0.0100 0.0033
20 1.1978 nan 0.0100 0.0027
40 1.1070 nan 0.0100 0.0020
60 1.0416 nan 0.0100 0.0015
80 0.9918 nan 0.0100 0.0009
100 0.9536 nan 0.0100 0.0008
120 0.9236 nan 0.0100 0.0004
140 0.8998 nan 0.0100 0.0005
160 0.8815 nan 0.0100 0.0004
180 0.8667 nan 0.0100 0.0002
200 0.8533 nan 0.0100 0.0003
220 0.8425 nan 0.0100 0.0002
240 0.8322 nan 0.0100 0.0001
260 0.8236 nan 0.0100 0.0000
280 0.8156 nan 0.0100 0.0001
300 0.8088 nan 0.0100 0.0000
320 0.8028 nan 0.0100 -0.0000
340 0.7969 nan 0.0100 -0.0000
360 0.7915 nan 0.0100 0.0000
380 0.7863 nan 0.0100 -0.0000
400 0.7815 nan 0.0100 -0.0001
420 0.7768 nan 0.0100 -0.0000
440 0.7727 nan 0.0100 0.0000
460 0.7687 nan 0.0100 -0.0001
480 0.7650 nan 0.0100 -0.0001
500 0.7612 nan 0.0100 -0.0001
520 0.7577 nan 0.0100 -0.0000
540 0.7544 nan 0.0100 -0.0001
560 0.7513 nan 0.0100 -0.0001
580 0.7480 nan 0.0100 -0.0001
600 0.7451 nan 0.0100 -0.0000
620 0.7426 nan 0.0100 -0.0001
640 0.7395 nan 0.0100 -0.0002
660 0.7370 nan 0.0100 -0.0001
680 0.7346 nan 0.0100 -0.0001
700 0.7322 nan 0.0100 -0.0001
720 0.7298 nan 0.0100 -0.0000
740 0.7277 nan 0.0100 -0.0001
760 0.7254 nan 0.0100 -0.0000
780 0.7230 nan 0.0100 -0.0001
800 0.7207 nan 0.0100 -0.0000
820 0.7180 nan 0.0100 -0.0001
840 0.7162 nan 0.0100 -0.0001
860 0.7141 nan 0.0100 -0.0001
880 0.7117 nan 0.0100 -0.0001
900 0.7096 nan 0.0100 -0.0000
920 0.7080 nan 0.0100 -0.0001
940 0.7061 nan 0.0100 -0.0001
960 0.7041 nan 0.0100 -0.0001
980 0.7024 nan 0.0100 -0.0001
1000 0.7003 nan 0.0100 -0.0000
1020 0.6983 nan 0.0100 -0.0001
1040 0.6966 nan 0.0100 -0.0001
1060 0.6946 nan 0.0100 0.0000
1080 0.6926 nan 0.0100 -0.0000
1100 0.6908 nan 0.0100 -0.0000
- Fold07.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3235 nan 0.0100 0.0042
2 1.3147 nan 0.0100 0.0044
3 1.3065 nan 0.0100 0.0040
4 1.2985 nan 0.0100 0.0038
5 1.2908 nan 0.0100 0.0039
6 1.2834 nan 0.0100 0.0035
7 1.2761 nan 0.0100 0.0036
8 1.2690 nan 0.0100 0.0033
9 1.2615 nan 0.0100 0.0038
10 1.2545 nan 0.0100 0.0034
20 1.1889 nan 0.0100 0.0029
40 1.0887 nan 0.0100 0.0020
60 1.0135 nan 0.0100 0.0015
80 0.9594 nan 0.0100 0.0011
100 0.9204 nan 0.0100 0.0007
120 0.8896 nan 0.0100 0.0005
140 0.8660 nan 0.0100 0.0004
160 0.8461 nan 0.0100 0.0003
180 0.8302 nan 0.0100 0.0001
200 0.8167 nan 0.0100 0.0003
220 0.8059 nan 0.0100 0.0001
240 0.7967 nan 0.0100 0.0001
260 0.7874 nan 0.0100 0.0001
280 0.7793 nan 0.0100 -0.0000
300 0.7717 nan 0.0100 -0.0000
320 0.7649 nan 0.0100 0.0001
340 0.7583 nan 0.0100 0.0000
360 0.7516 nan 0.0100 -0.0000
380 0.7453 nan 0.0100 -0.0002
400 0.7396 nan 0.0100 -0.0001
420 0.7345 nan 0.0100 -0.0001
440 0.7293 nan 0.0100 -0.0001
460 0.7243 nan 0.0100 -0.0000
480 0.7195 nan 0.0100 0.0000
500 0.7159 nan 0.0100 -0.0002
520 0.7111 nan 0.0100 -0.0002
540 0.7073 nan 0.0100 -0.0000
560 0.7036 nan 0.0100 -0.0000
580 0.7001 nan 0.0100 -0.0001
600 0.6967 nan 0.0100 -0.0002
620 0.6933 nan 0.0100 0.0000
640 0.6901 nan 0.0100 -0.0001
660 0.6866 nan 0.0100 -0.0002
680 0.6831 nan 0.0100 -0.0001
700 0.6802 nan 0.0100 -0.0001
720 0.6769 nan 0.0100 0.0000
740 0.6736 nan 0.0100 -0.0000
760 0.6699 nan 0.0100 -0.0001
780 0.6665 nan 0.0100 -0.0001
800 0.6636 nan 0.0100 -0.0002
820 0.6609 nan 0.0100 0.0000
840 0.6580 nan 0.0100 -0.0002
860 0.6553 nan 0.0100 -0.0001
880 0.6522 nan 0.0100 -0.0001
900 0.6501 nan 0.0100 -0.0002
920 0.6473 nan 0.0100 -0.0001
940 0.6449 nan 0.0100 -0.0001
960 0.6423 nan 0.0100 -0.0001
980 0.6394 nan 0.0100 -0.0000
1000 0.6374 nan 0.0100 -0.0001
1020 0.6348 nan 0.0100 -0.0001
1040 0.6322 nan 0.0100 -0.0001
1060 0.6294 nan 0.0100 -0.0001
1080 0.6275 nan 0.0100 -0.0001
1100 0.6251 nan 0.0100 -0.0001
- Fold07.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2691 nan 0.1000 0.0303
2 1.2186 nan 0.1000 0.0244
3 1.1752 nan 0.1000 0.0191
4 1.1435 nan 0.1000 0.0149
5 1.1146 nan 0.1000 0.0135
6 1.0893 nan 0.1000 0.0106
7 1.0701 nan 0.1000 0.0085
8 1.0518 nan 0.1000 0.0078
9 1.0337 nan 0.1000 0.0073
10 1.0208 nan 0.1000 0.0067
20 0.9263 nan 0.1000 0.0019
40 0.8544 nan 0.1000 -0.0001
60 0.8236 nan 0.1000 -0.0003
80 0.8026 nan 0.1000 -0.0000
100 0.7841 nan 0.1000 -0.0007
120 0.7731 nan 0.1000 -0.0006
140 0.7636 nan 0.1000 -0.0009
160 0.7544 nan 0.1000 -0.0013
180 0.7482 nan 0.1000 -0.0011
200 0.7410 nan 0.1000 -0.0007
220 0.7362 nan 0.1000 -0.0001
240 0.7311 nan 0.1000 -0.0006
260 0.7270 nan 0.1000 -0.0012
280 0.7230 nan 0.1000 -0.0009
300 0.7189 nan 0.1000 -0.0009
320 0.7133 nan 0.1000 -0.0003
340 0.7105 nan 0.1000 -0.0006
360 0.7071 nan 0.1000 -0.0014
380 0.7034 nan 0.1000 -0.0004
400 0.7002 nan 0.1000 -0.0004
420 0.6982 nan 0.1000 -0.0012
440 0.6950 nan 0.1000 -0.0006
460 0.6919 nan 0.1000 -0.0006
480 0.6888 nan 0.1000 -0.0008
500 0.6850 nan 0.1000 -0.0009
520 0.6827 nan 0.1000 -0.0013
540 0.6808 nan 0.1000 -0.0006
560 0.6763 nan 0.1000 -0.0004
580 0.6734 nan 0.1000 -0.0003
600 0.6709 nan 0.1000 -0.0009
620 0.6696 nan 0.1000 -0.0010
640 0.6672 nan 0.1000 -0.0006
660 0.6648 nan 0.1000 -0.0007
680 0.6620 nan 0.1000 -0.0012
700 0.6596 nan 0.1000 -0.0009
720 0.6572 nan 0.1000 -0.0005
740 0.6561 nan 0.1000 -0.0005
760 0.6549 nan 0.1000 -0.0007
780 0.6524 nan 0.1000 -0.0007
800 0.6507 nan 0.1000 -0.0009
820 0.6503 nan 0.1000 -0.0011
840 0.6482 nan 0.1000 -0.0007
860 0.6473 nan 0.1000 -0.0007
880 0.6460 nan 0.1000 -0.0005
900 0.6447 nan 0.1000 -0.0014
920 0.6435 nan 0.1000 -0.0011
940 0.6421 nan 0.1000 -0.0012
960 0.6406 nan 0.1000 -0.0008
980 0.6389 nan 0.1000 -0.0009
1000 0.6375 nan 0.1000 -0.0015
1020 0.6372 nan 0.1000 -0.0006
1040 0.6364 nan 0.1000 -0.0007
1060 0.6345 nan 0.1000 -0.0008
1080 0.6342 nan 0.1000 -0.0012
1100 0.6325 nan 0.1000 -0.0002
- Fold07.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2511 nan 0.1000 0.0352
2 1.1947 nan 0.1000 0.0300
3 1.1390 nan 0.1000 0.0226
4 1.0988 nan 0.1000 0.0174
5 1.0634 nan 0.1000 0.0166
6 1.0352 nan 0.1000 0.0144
7 1.0092 nan 0.1000 0.0132
8 0.9856 nan 0.1000 0.0109
9 0.9660 nan 0.1000 0.0089
10 0.9472 nan 0.1000 0.0083
20 0.8536 nan 0.1000 0.0015
40 0.7804 nan 0.1000 0.0013
60 0.7480 nan 0.1000 -0.0021
80 0.7230 nan 0.1000 -0.0008
100 0.6999 nan 0.1000 -0.0001
120 0.6854 nan 0.1000 -0.0012
140 0.6726 nan 0.1000 -0.0014
160 0.6590 nan 0.1000 -0.0013
180 0.6449 nan 0.1000 -0.0008
200 0.6295 nan 0.1000 -0.0013
220 0.6175 nan 0.1000 -0.0011
240 0.6077 nan 0.1000 -0.0017
260 0.5972 nan 0.1000 -0.0009
280 0.5864 nan 0.1000 -0.0001
300 0.5772 nan 0.1000 -0.0010
320 0.5682 nan 0.1000 -0.0005
340 0.5611 nan 0.1000 -0.0009
360 0.5552 nan 0.1000 -0.0007
380 0.5464 nan 0.1000 -0.0011
400 0.5401 nan 0.1000 -0.0005
420 0.5322 nan 0.1000 -0.0007
440 0.5273 nan 0.1000 -0.0010
460 0.5225 nan 0.1000 -0.0017
480 0.5156 nan 0.1000 -0.0011
500 0.5106 nan 0.1000 -0.0007
520 0.5059 nan 0.1000 -0.0007
540 0.5023 nan 0.1000 -0.0007
560 0.4981 nan 0.1000 -0.0007
580 0.4927 nan 0.1000 -0.0007
600 0.4866 nan 0.1000 -0.0007
620 0.4823 nan 0.1000 -0.0005
640 0.4780 nan 0.1000 -0.0006
660 0.4738 nan 0.1000 -0.0007
680 0.4693 nan 0.1000 -0.0009
700 0.4634 nan 0.1000 -0.0019
720 0.4581 nan 0.1000 -0.0009
740 0.4541 nan 0.1000 -0.0007
760 0.4493 nan 0.1000 -0.0014
780 0.4435 nan 0.1000 -0.0010
800 0.4407 nan 0.1000 -0.0009
820 0.4373 nan 0.1000 -0.0011
840 0.4329 nan 0.1000 -0.0009
860 0.4281 nan 0.1000 -0.0011
880 0.4242 nan 0.1000 -0.0006
900 0.4208 nan 0.1000 -0.0008
920 0.4172 nan 0.1000 -0.0007
940 0.4132 nan 0.1000 -0.0013
960 0.4100 nan 0.1000 -0.0015
980 0.4069 nan 0.1000 -0.0019
1000 0.4039 nan 0.1000 -0.0015
1020 0.4003 nan 0.1000 -0.0007
1040 0.3977 nan 0.1000 -0.0006
1060 0.3941 nan 0.1000 -0.0002
1080 0.3915 nan 0.1000 -0.0007
1100 0.3882 nan 0.1000 -0.0010
- Fold07.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2474 nan 0.1000 0.0385
2 1.1788 nan 0.1000 0.0343
3 1.1258 nan 0.1000 0.0250
4 1.0770 nan 0.1000 0.0194
5 1.0380 nan 0.1000 0.0174
6 1.0036 nan 0.1000 0.0126
7 0.9734 nan 0.1000 0.0141
8 0.9497 nan 0.1000 0.0108
9 0.9283 nan 0.1000 0.0079
10 0.9092 nan 0.1000 0.0096
20 0.8129 nan 0.1000 0.0034
40 0.7388 nan 0.1000 -0.0001
60 0.6964 nan 0.1000 -0.0016
80 0.6696 nan 0.1000 -0.0012
100 0.6426 nan 0.1000 -0.0011
120 0.6215 nan 0.1000 -0.0014
140 0.5992 nan 0.1000 -0.0006
160 0.5826 nan 0.1000 -0.0020
180 0.5639 nan 0.1000 -0.0017
200 0.5489 nan 0.1000 -0.0007
220 0.5343 nan 0.1000 -0.0012
240 0.5223 nan 0.1000 -0.0007
260 0.5090 nan 0.1000 -0.0013
280 0.4971 nan 0.1000 -0.0007
300 0.4873 nan 0.1000 -0.0010
320 0.4780 nan 0.1000 -0.0017
340 0.4657 nan 0.1000 -0.0014
360 0.4555 nan 0.1000 -0.0010
380 0.4471 nan 0.1000 -0.0012
400 0.4396 nan 0.1000 -0.0019
420 0.4307 nan 0.1000 -0.0015
440 0.4236 nan 0.1000 -0.0011
460 0.4155 nan 0.1000 -0.0006
480 0.4090 nan 0.1000 -0.0008
500 0.4017 nan 0.1000 -0.0010
520 0.3943 nan 0.1000 -0.0010
540 0.3858 nan 0.1000 -0.0011
560 0.3795 nan 0.1000 -0.0012
580 0.3713 nan 0.1000 -0.0010
600 0.3630 nan 0.1000 -0.0007
620 0.3571 nan 0.1000 -0.0006
640 0.3503 nan 0.1000 -0.0014
660 0.3450 nan 0.1000 -0.0008
680 0.3411 nan 0.1000 -0.0009
700 0.3356 nan 0.1000 -0.0014
720 0.3267 nan 0.1000 -0.0007
740 0.3219 nan 0.1000 -0.0005
760 0.3164 nan 0.1000 -0.0008
780 0.3105 nan 0.1000 -0.0004
800 0.3043 nan 0.1000 -0.0005
820 0.3012 nan 0.1000 -0.0013
840 0.2952 nan 0.1000 -0.0015
860 0.2901 nan 0.1000 -0.0007
880 0.2849 nan 0.1000 -0.0006
900 0.2820 nan 0.1000 -0.0004
920 0.2790 nan 0.1000 -0.0006
940 0.2750 nan 0.1000 -0.0004
960 0.2724 nan 0.1000 -0.0007
980 0.2692 nan 0.1000 -0.0011
1000 0.2661 nan 0.1000 -0.0005
1020 0.2636 nan 0.1000 -0.0016
1040 0.2617 nan 0.1000 -0.0006
1060 0.2584 nan 0.1000 -0.0008
1080 0.2550 nan 0.1000 -0.0006
1100 0.2513 nan 0.1000 -0.0005
- Fold07.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3259 nan 0.0100 0.0030
2 1.3199 nan 0.0100 0.0030
3 1.3137 nan 0.0100 0.0029
4 1.3075 nan 0.0100 0.0029
5 1.3016 nan 0.0100 0.0028
6 1.2963 nan 0.0100 0.0028
7 1.2910 nan 0.0100 0.0027
8 1.2856 nan 0.0100 0.0027
9 1.2791 nan 0.0100 0.0027
10 1.2739 nan 0.0100 0.0026
20 1.2257 nan 0.0100 0.0021
40 1.1550 nan 0.0100 0.0015
60 1.1050 nan 0.0100 0.0011
80 1.0663 nan 0.0100 0.0007
100 1.0353 nan 0.0100 0.0006
120 1.0103 nan 0.0100 0.0005
140 0.9886 nan 0.0100 0.0003
160 0.9705 nan 0.0100 0.0004
180 0.9551 nan 0.0100 0.0003
200 0.9415 nan 0.0100 0.0002
220 0.9290 nan 0.0100 0.0002
240 0.9175 nan 0.0100 0.0002
260 0.9078 nan 0.0100 0.0002
280 0.8987 nan 0.0100 0.0002
300 0.8910 nan 0.0100 0.0000
320 0.8838 nan 0.0100 0.0001
340 0.8773 nan 0.0100 0.0001
360 0.8710 nan 0.0100 0.0001
380 0.8657 nan 0.0100 0.0000
400 0.8599 nan 0.0100 0.0001
420 0.8552 nan 0.0100 0.0000
440 0.8504 nan 0.0100 0.0000
460 0.8460 nan 0.0100 -0.0000
480 0.8417 nan 0.0100 0.0001
500 0.8375 nan 0.0100 -0.0000
520 0.8336 nan 0.0100 -0.0001
540 0.8298 nan 0.0100 0.0001
560 0.8263 nan 0.0100 -0.0001
580 0.8230 nan 0.0100 0.0001
600 0.8197 nan 0.0100 -0.0000
620 0.8164 nan 0.0100 -0.0000
640 0.8133 nan 0.0100 -0.0001
660 0.8104 nan 0.0100 0.0000
680 0.8079 nan 0.0100 -0.0000
700 0.8054 nan 0.0100 0.0000
720 0.8031 nan 0.0100 0.0000
740 0.8006 nan 0.0100 -0.0000
760 0.7985 nan 0.0100 -0.0001
780 0.7962 nan 0.0100 -0.0000
800 0.7939 nan 0.0100 0.0000
820 0.7919 nan 0.0100 0.0000
840 0.7897 nan 0.0100 -0.0001
860 0.7878 nan 0.0100 -0.0001
880 0.7859 nan 0.0100 -0.0000
900 0.7839 nan 0.0100 -0.0001
920 0.7821 nan 0.0100 -0.0000
940 0.7802 nan 0.0100 -0.0001
960 0.7787 nan 0.0100 -0.0000
980 0.7772 nan 0.0100 0.0000
1000 0.7756 nan 0.0100 -0.0000
1020 0.7740 nan 0.0100 -0.0001
1040 0.7725 nan 0.0100 -0.0001
1060 0.7708 nan 0.0100 -0.0000
1080 0.7694 nan 0.0100 -0.0000
1100 0.7681 nan 0.0100 -0.0001
- Fold08.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3241 nan 0.0100 0.0038
2 1.3168 nan 0.0100 0.0034
3 1.3095 nan 0.0100 0.0037
4 1.3020 nan 0.0100 0.0036
5 1.2952 nan 0.0100 0.0034
6 1.2886 nan 0.0100 0.0034
7 1.2816 nan 0.0100 0.0035
8 1.2744 nan 0.0100 0.0032
9 1.2680 nan 0.0100 0.0034
10 1.2620 nan 0.0100 0.0030
20 1.2047 nan 0.0100 0.0024
40 1.1138 nan 0.0100 0.0020
60 1.0469 nan 0.0100 0.0012
80 0.9978 nan 0.0100 0.0011
100 0.9603 nan 0.0100 0.0008
120 0.9308 nan 0.0100 0.0006
140 0.9059 nan 0.0100 0.0004
160 0.8870 nan 0.0100 0.0002
180 0.8704 nan 0.0100 0.0003
200 0.8569 nan 0.0100 0.0003
220 0.8451 nan 0.0100 0.0003
240 0.8336 nan 0.0100 0.0001
260 0.8241 nan 0.0100 -0.0000
280 0.8162 nan 0.0100 0.0002
300 0.8087 nan 0.0100 0.0000
320 0.8022 nan 0.0100 0.0000
340 0.7954 nan 0.0100 0.0000
360 0.7889 nan 0.0100 0.0000
380 0.7836 nan 0.0100 -0.0000
400 0.7781 nan 0.0100 0.0000
420 0.7726 nan 0.0100 0.0001
440 0.7677 nan 0.0100 0.0000
460 0.7635 nan 0.0100 -0.0001
480 0.7592 nan 0.0100 -0.0000
500 0.7555 nan 0.0100 0.0001
520 0.7514 nan 0.0100 -0.0000
540 0.7479 nan 0.0100 -0.0000
560 0.7445 nan 0.0100 0.0001
580 0.7409 nan 0.0100 0.0000
600 0.7377 nan 0.0100 -0.0000
620 0.7340 nan 0.0100 -0.0000
640 0.7312 nan 0.0100 -0.0000
660 0.7282 nan 0.0100 -0.0002
680 0.7254 nan 0.0100 -0.0000
700 0.7226 nan 0.0100 -0.0001
720 0.7198 nan 0.0100 -0.0001
740 0.7171 nan 0.0100 -0.0000
760 0.7147 nan 0.0100 -0.0001
780 0.7117 nan 0.0100 -0.0001
800 0.7093 nan 0.0100 -0.0001
820 0.7073 nan 0.0100 -0.0002
840 0.7050 nan 0.0100 -0.0001
860 0.7029 nan 0.0100 -0.0000
880 0.7009 nan 0.0100 -0.0000
900 0.6987 nan 0.0100 -0.0000
920 0.6966 nan 0.0100 -0.0001
940 0.6943 nan 0.0100 -0.0002
960 0.6920 nan 0.0100 -0.0000
980 0.6902 nan 0.0100 -0.0001
1000 0.6884 nan 0.0100 -0.0001
1020 0.6867 nan 0.0100 -0.0000
1040 0.6848 nan 0.0100 -0.0000
1060 0.6833 nan 0.0100 -0.0002
1080 0.6812 nan 0.0100 -0.0001
1100 0.6793 nan 0.0100 -0.0000
- Fold08.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0041
2 1.3160 nan 0.0100 0.0041
3 1.3076 nan 0.0100 0.0041
4 1.2996 nan 0.0100 0.0037
5 1.2918 nan 0.0100 0.0034
6 1.2849 nan 0.0100 0.0037
7 1.2776 nan 0.0100 0.0032
8 1.2704 nan 0.0100 0.0036
9 1.2627 nan 0.0100 0.0032
10 1.2555 nan 0.0100 0.0035
20 1.1912 nan 0.0100 0.0029
40 1.0931 nan 0.0100 0.0022
60 1.0193 nan 0.0100 0.0014
80 0.9655 nan 0.0100 0.0009
100 0.9241 nan 0.0100 0.0008
120 0.8939 nan 0.0100 0.0005
140 0.8688 nan 0.0100 0.0004
160 0.8492 nan 0.0100 0.0003
180 0.8333 nan 0.0100 0.0001
200 0.8186 nan 0.0100 0.0002
220 0.8049 nan 0.0100 0.0000
240 0.7937 nan 0.0100 0.0001
260 0.7833 nan 0.0100 0.0002
280 0.7746 nan 0.0100 0.0000
300 0.7664 nan 0.0100 0.0000
320 0.7585 nan 0.0100 -0.0000
340 0.7520 nan 0.0100 -0.0000
360 0.7455 nan 0.0100 -0.0001
380 0.7394 nan 0.0100 -0.0000
400 0.7336 nan 0.0100 -0.0001
420 0.7281 nan 0.0100 -0.0001
440 0.7235 nan 0.0100 -0.0000
460 0.7185 nan 0.0100 -0.0001
480 0.7147 nan 0.0100 -0.0000
500 0.7101 nan 0.0100 -0.0001
520 0.7058 nan 0.0100 -0.0000
540 0.7013 nan 0.0100 -0.0001
560 0.6976 nan 0.0100 0.0000
580 0.6939 nan 0.0100 -0.0001
600 0.6906 nan 0.0100 -0.0001
620 0.6865 nan 0.0100 -0.0002
640 0.6829 nan 0.0100 0.0000
660 0.6796 nan 0.0100 -0.0000
680 0.6763 nan 0.0100 -0.0000
700 0.6726 nan 0.0100 0.0000
720 0.6695 nan 0.0100 -0.0000
740 0.6663 nan 0.0100 -0.0001
760 0.6635 nan 0.0100 -0.0001
780 0.6605 nan 0.0100 0.0000
800 0.6576 nan 0.0100 -0.0002
820 0.6548 nan 0.0100 -0.0002
840 0.6522 nan 0.0100 -0.0001
860 0.6494 nan 0.0100 -0.0001
880 0.6467 nan 0.0100 -0.0001
900 0.6435 nan 0.0100 -0.0001
920 0.6409 nan 0.0100 -0.0001
940 0.6385 nan 0.0100 -0.0002
960 0.6361 nan 0.0100 -0.0001
980 0.6336 nan 0.0100 -0.0001
1000 0.6312 nan 0.0100 -0.0001
1020 0.6289 nan 0.0100 -0.0001
1040 0.6267 nan 0.0100 -0.0001
1060 0.6247 nan 0.0100 -0.0001
1080 0.6223 nan 0.0100 -0.0001
1100 0.6200 nan 0.0100 -0.0001
- Fold08.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2761 nan 0.1000 0.0294
2 1.2276 nan 0.1000 0.0232
3 1.1875 nan 0.1000 0.0204
4 1.1550 nan 0.1000 0.0170
5 1.1289 nan 0.1000 0.0140
6 1.1114 nan 0.1000 0.0078
7 1.0904 nan 0.1000 0.0113
8 1.0698 nan 0.1000 0.0095
9 1.0543 nan 0.1000 0.0083
10 1.0394 nan 0.1000 0.0071
20 0.9376 nan 0.1000 0.0033
40 0.8591 nan 0.1000 0.0001
60 0.8187 nan 0.1000 -0.0010
80 0.7946 nan 0.1000 -0.0003
100 0.7775 nan 0.1000 -0.0011
120 0.7636 nan 0.1000 -0.0001
140 0.7534 nan 0.1000 -0.0004
160 0.7454 nan 0.1000 -0.0006
180 0.7346 nan 0.1000 -0.0011
200 0.7267 nan 0.1000 -0.0012
220 0.7220 nan 0.1000 -0.0008
240 0.7159 nan 0.1000 -0.0003
260 0.7112 nan 0.1000 -0.0002
280 0.7062 nan 0.1000 -0.0009
300 0.7013 nan 0.1000 -0.0010
320 0.6974 nan 0.1000 -0.0010
340 0.6932 nan 0.1000 -0.0013
360 0.6893 nan 0.1000 -0.0007
380 0.6860 nan 0.1000 -0.0013
400 0.6831 nan 0.1000 -0.0005
420 0.6804 nan 0.1000 -0.0008
440 0.6768 nan 0.1000 -0.0009
460 0.6732 nan 0.1000 -0.0008
480 0.6718 nan 0.1000 -0.0004
500 0.6694 nan 0.1000 -0.0007
520 0.6664 nan 0.1000 -0.0008
540 0.6643 nan 0.1000 -0.0005
560 0.6626 nan 0.1000 -0.0007
580 0.6605 nan 0.1000 -0.0009
600 0.6575 nan 0.1000 -0.0010
620 0.6559 nan 0.1000 -0.0011
640 0.6536 nan 0.1000 -0.0009
660 0.6502 nan 0.1000 -0.0011
680 0.6480 nan 0.1000 -0.0008
700 0.6464 nan 0.1000 -0.0013
720 0.6448 nan 0.1000 -0.0007
740 0.6437 nan 0.1000 -0.0004
760 0.6422 nan 0.1000 -0.0004
780 0.6404 nan 0.1000 -0.0006
800 0.6390 nan 0.1000 -0.0007
820 0.6365 nan 0.1000 -0.0008
840 0.6339 nan 0.1000 -0.0003
860 0.6325 nan 0.1000 -0.0005
880 0.6311 nan 0.1000 -0.0012
900 0.6299 nan 0.1000 -0.0003
920 0.6283 nan 0.1000 -0.0010
940 0.6256 nan 0.1000 -0.0005
960 0.6252 nan 0.1000 -0.0004
980 0.6227 nan 0.1000 -0.0006
1000 0.6221 nan 0.1000 -0.0006
1020 0.6206 nan 0.1000 -0.0005
1040 0.6196 nan 0.1000 -0.0005
1060 0.6186 nan 0.1000 -0.0005
1080 0.6174 nan 0.1000 -0.0006
1100 0.6148 nan 0.1000 -0.0005
- Fold08.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2543 nan 0.1000 0.0347
2 1.1960 nan 0.1000 0.0274
3 1.1435 nan 0.1000 0.0248
4 1.1018 nan 0.1000 0.0194
5 1.0646 nan 0.1000 0.0160
6 1.0315 nan 0.1000 0.0132
7 1.0038 nan 0.1000 0.0124
8 0.9831 nan 0.1000 0.0094
9 0.9640 nan 0.1000 0.0091
10 0.9492 nan 0.1000 0.0053
20 0.8516 nan 0.1000 0.0020
40 0.7780 nan 0.1000 0.0004
60 0.7435 nan 0.1000 -0.0004
80 0.7137 nan 0.1000 -0.0021
100 0.6890 nan 0.1000 -0.0006
120 0.6715 nan 0.1000 -0.0007
140 0.6553 nan 0.1000 -0.0015
160 0.6426 nan 0.1000 -0.0013
180 0.6303 nan 0.1000 -0.0007
200 0.6183 nan 0.1000 -0.0004
220 0.6066 nan 0.1000 -0.0009
240 0.5947 nan 0.1000 -0.0007
260 0.5843 nan 0.1000 -0.0005
280 0.5765 nan 0.1000 -0.0013
300 0.5692 nan 0.1000 -0.0006
320 0.5597 nan 0.1000 -0.0008
340 0.5516 nan 0.1000 -0.0019
360 0.5456 nan 0.1000 -0.0012
380 0.5371 nan 0.1000 -0.0010
400 0.5294 nan 0.1000 -0.0010
420 0.5201 nan 0.1000 -0.0009
440 0.5126 nan 0.1000 -0.0011
460 0.5076 nan 0.1000 -0.0014
480 0.5008 nan 0.1000 -0.0010
500 0.4953 nan 0.1000 -0.0003
520 0.4890 nan 0.1000 -0.0006
540 0.4834 nan 0.1000 -0.0008
560 0.4786 nan 0.1000 -0.0016
580 0.4747 nan 0.1000 -0.0007
600 0.4703 nan 0.1000 -0.0006
620 0.4648 nan 0.1000 -0.0006
640 0.4611 nan 0.1000 -0.0009
660 0.4554 nan 0.1000 -0.0003
680 0.4523 nan 0.1000 -0.0007
700 0.4486 nan 0.1000 -0.0016
720 0.4447 nan 0.1000 -0.0004
740 0.4425 nan 0.1000 -0.0003
760 0.4376 nan 0.1000 -0.0012
780 0.4334 nan 0.1000 -0.0006
800 0.4307 nan 0.1000 -0.0007
820 0.4265 nan 0.1000 -0.0011
840 0.4234 nan 0.1000 -0.0015
860 0.4207 nan 0.1000 -0.0013
880 0.4159 nan 0.1000 -0.0007
900 0.4130 nan 0.1000 -0.0013
920 0.4103 nan 0.1000 -0.0008
940 0.4052 nan 0.1000 -0.0006
960 0.4016 nan 0.1000 -0.0004
980 0.3974 nan 0.1000 -0.0005
1000 0.3941 nan 0.1000 -0.0014
1020 0.3909 nan 0.1000 -0.0003
1040 0.3877 nan 0.1000 -0.0009
1060 0.3838 nan 0.1000 -0.0012
1080 0.3814 nan 0.1000 -0.0008
1100 0.3782 nan 0.1000 -0.0004
- Fold08.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2491 nan 0.1000 0.0373
2 1.1870 nan 0.1000 0.0285
3 1.1347 nan 0.1000 0.0256
4 1.0853 nan 0.1000 0.0210
5 1.0503 nan 0.1000 0.0187
6 1.0159 nan 0.1000 0.0173
7 0.9859 nan 0.1000 0.0146
8 0.9598 nan 0.1000 0.0113
9 0.9383 nan 0.1000 0.0073
10 0.9218 nan 0.1000 0.0081
20 0.8173 nan 0.1000 0.0015
40 0.7387 nan 0.1000 -0.0006
60 0.6924 nan 0.1000 -0.0012
80 0.6612 nan 0.1000 -0.0014
100 0.6325 nan 0.1000 -0.0017
120 0.6108 nan 0.1000 -0.0005
140 0.5893 nan 0.1000 -0.0027
160 0.5721 nan 0.1000 -0.0020
180 0.5555 nan 0.1000 -0.0017
200 0.5406 nan 0.1000 -0.0017
220 0.5270 nan 0.1000 -0.0016
240 0.5153 nan 0.1000 -0.0021
260 0.5024 nan 0.1000 -0.0006
280 0.4926 nan 0.1000 -0.0024
300 0.4818 nan 0.1000 -0.0006
320 0.4699 nan 0.1000 -0.0005
340 0.4600 nan 0.1000 -0.0003
360 0.4522 nan 0.1000 -0.0009
380 0.4404 nan 0.1000 -0.0002
400 0.4300 nan 0.1000 -0.0009
420 0.4234 nan 0.1000 -0.0009
440 0.4135 nan 0.1000 -0.0008
460 0.4091 nan 0.1000 -0.0017
480 0.4014 nan 0.1000 -0.0013
500 0.3946 nan 0.1000 -0.0010
520 0.3888 nan 0.1000 -0.0013
540 0.3836 nan 0.1000 -0.0008
560 0.3793 nan 0.1000 -0.0007
580 0.3725 nan 0.1000 -0.0013
600 0.3664 nan 0.1000 -0.0006
620 0.3610 nan 0.1000 -0.0016
640 0.3540 nan 0.1000 -0.0013
660 0.3497 nan 0.1000 -0.0010
680 0.3456 nan 0.1000 -0.0008
700 0.3397 nan 0.1000 -0.0011
720 0.3358 nan 0.1000 -0.0014
740 0.3323 nan 0.1000 -0.0015
760 0.3268 nan 0.1000 -0.0012
780 0.3213 nan 0.1000 -0.0012
800 0.3163 nan 0.1000 -0.0017
820 0.3125 nan 0.1000 -0.0010
840 0.3087 nan 0.1000 -0.0014
860 0.3041 nan 0.1000 -0.0009
880 0.3000 nan 0.1000 -0.0005
900 0.2949 nan 0.1000 -0.0013
920 0.2923 nan 0.1000 -0.0012
940 0.2868 nan 0.1000 -0.0014
960 0.2834 nan 0.1000 -0.0006
980 0.2790 nan 0.1000 -0.0005
1000 0.2752 nan 0.1000 -0.0009
1020 0.2725 nan 0.1000 -0.0009
1040 0.2700 nan 0.1000 -0.0008
1060 0.2663 nan 0.1000 -0.0009
1080 0.2641 nan 0.1000 -0.0010
1100 0.2601 nan 0.1000 -0.0012
- Fold08.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3269 nan 0.0100 0.0028
2 1.3206 nan 0.0100 0.0029
3 1.3144 nan 0.0100 0.0029
4 1.3085 nan 0.0100 0.0028
5 1.3026 nan 0.0100 0.0027
6 1.2968 nan 0.0100 0.0026
7 1.2916 nan 0.0100 0.0026
8 1.2866 nan 0.0100 0.0025
9 1.2813 nan 0.0100 0.0025
10 1.2760 nan 0.0100 0.0025
20 1.2321 nan 0.0100 0.0019
40 1.1649 nan 0.0100 0.0014
60 1.1177 nan 0.0100 0.0010
80 1.0824 nan 0.0100 0.0007
100 1.0539 nan 0.0100 0.0006
120 1.0301 nan 0.0100 0.0004
140 1.0107 nan 0.0100 0.0004
160 0.9941 nan 0.0100 0.0003
180 0.9796 nan 0.0100 0.0003
200 0.9671 nan 0.0100 0.0001
220 0.9563 nan 0.0100 0.0002
240 0.9458 nan 0.0100 0.0002
260 0.9363 nan 0.0100 0.0002
280 0.9277 nan 0.0100 0.0001
300 0.9204 nan 0.0100 0.0001
320 0.9138 nan 0.0100 0.0001
340 0.9077 nan 0.0100 0.0001
360 0.9015 nan 0.0100 0.0001
380 0.8963 nan 0.0100 0.0001
400 0.8915 nan 0.0100 0.0000
420 0.8868 nan 0.0100 0.0001
440 0.8821 nan 0.0100 0.0000
460 0.8779 nan 0.0100 0.0000
480 0.8738 nan 0.0100 0.0000
500 0.8699 nan 0.0100 -0.0000
520 0.8663 nan 0.0100 0.0000
540 0.8627 nan 0.0100 0.0000
560 0.8594 nan 0.0100 0.0000
580 0.8562 nan 0.0100 0.0000
600 0.8535 nan 0.0100 -0.0000
620 0.8509 nan 0.0100 -0.0000
640 0.8480 nan 0.0100 0.0000
660 0.8452 nan 0.0100 -0.0000
680 0.8426 nan 0.0100 -0.0001
700 0.8403 nan 0.0100 -0.0001
720 0.8378 nan 0.0100 -0.0000
740 0.8355 nan 0.0100 -0.0000
760 0.8332 nan 0.0100 0.0000
780 0.8310 nan 0.0100 -0.0001
800 0.8291 nan 0.0100 -0.0000
820 0.8269 nan 0.0100 -0.0000
840 0.8248 nan 0.0100 0.0000
860 0.8227 nan 0.0100 -0.0000
880 0.8208 nan 0.0100 -0.0000
900 0.8191 nan 0.0100 -0.0000
920 0.8174 nan 0.0100 -0.0001
940 0.8159 nan 0.0100 -0.0000
960 0.8143 nan 0.0100 -0.0001
980 0.8127 nan 0.0100 -0.0001
1000 0.8112 nan 0.0100 -0.0001
1020 0.8096 nan 0.0100 -0.0001
1040 0.8081 nan 0.0100 -0.0000
1060 0.8068 nan 0.0100 0.0000
1080 0.8054 nan 0.0100 -0.0001
1100 0.8041 nan 0.0100 -0.0000
- Fold09.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3249 nan 0.0100 0.0034
2 1.3172 nan 0.0100 0.0033
3 1.3097 nan 0.0100 0.0035
4 1.3025 nan 0.0100 0.0033
5 1.2960 nan 0.0100 0.0033
6 1.2892 nan 0.0100 0.0032
7 1.2828 nan 0.0100 0.0028
8 1.2763 nan 0.0100 0.0031
9 1.2709 nan 0.0100 0.0027
10 1.2646 nan 0.0100 0.0030
20 1.2100 nan 0.0100 0.0025
40 1.1249 nan 0.0100 0.0018
60 1.0621 nan 0.0100 0.0013
80 1.0159 nan 0.0100 0.0009
100 0.9812 nan 0.0100 0.0006
120 0.9547 nan 0.0100 0.0004
140 0.9334 nan 0.0100 0.0004
160 0.9158 nan 0.0100 0.0003
180 0.9011 nan 0.0100 0.0001
200 0.8882 nan 0.0100 0.0002
220 0.8774 nan 0.0100 0.0002
240 0.8672 nan 0.0100 0.0001
260 0.8581 nan 0.0100 0.0001
280 0.8491 nan 0.0100 0.0001
300 0.8409 nan 0.0100 0.0001
320 0.8341 nan 0.0100 0.0001
340 0.8271 nan 0.0100 0.0002
360 0.8207 nan 0.0100 0.0000
380 0.8150 nan 0.0100 -0.0000
400 0.8093 nan 0.0100 -0.0000
420 0.8043 nan 0.0100 -0.0000
440 0.7995 nan 0.0100 -0.0001
460 0.7948 nan 0.0100 0.0001
480 0.7909 nan 0.0100 -0.0002
500 0.7867 nan 0.0100 -0.0000
520 0.7833 nan 0.0100 -0.0001
540 0.7792 nan 0.0100 0.0000
560 0.7757 nan 0.0100 -0.0002
580 0.7722 nan 0.0100 -0.0001
600 0.7690 nan 0.0100 -0.0000
620 0.7655 nan 0.0100 -0.0001
640 0.7623 nan 0.0100 -0.0001
660 0.7595 nan 0.0100 -0.0001
680 0.7564 nan 0.0100 -0.0001
700 0.7541 nan 0.0100 -0.0001
720 0.7518 nan 0.0100 -0.0001
740 0.7494 nan 0.0100 -0.0000
760 0.7469 nan 0.0100 -0.0001
780 0.7445 nan 0.0100 -0.0000
800 0.7424 nan 0.0100 -0.0000
820 0.7404 nan 0.0100 -0.0000
840 0.7380 nan 0.0100 0.0000
860 0.7359 nan 0.0100 -0.0001
880 0.7342 nan 0.0100 -0.0001
900 0.7322 nan 0.0100 -0.0000
920 0.7299 nan 0.0100 -0.0000
940 0.7275 nan 0.0100 -0.0000
960 0.7253 nan 0.0100 -0.0001
980 0.7234 nan 0.0100 -0.0000
1000 0.7217 nan 0.0100 -0.0002
1020 0.7199 nan 0.0100 -0.0001
1040 0.7180 nan 0.0100 -0.0001
1060 0.7161 nan 0.0100 -0.0000
1080 0.7147 nan 0.0100 -0.0000
1100 0.7125 nan 0.0100 -0.0000
- Fold09.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3236 nan 0.0100 0.0038
2 1.3158 nan 0.0100 0.0038
3 1.3079 nan 0.0100 0.0039
4 1.3010 nan 0.0100 0.0036
5 1.2935 nan 0.0100 0.0035
6 1.2863 nan 0.0100 0.0036
7 1.2791 nan 0.0100 0.0033
8 1.2723 nan 0.0100 0.0033
9 1.2653 nan 0.0100 0.0033
10 1.2591 nan 0.0100 0.0032
20 1.1987 nan 0.0100 0.0027
40 1.1039 nan 0.0100 0.0019
60 1.0362 nan 0.0100 0.0014
80 0.9867 nan 0.0100 0.0010
100 0.9474 nan 0.0100 0.0006
120 0.9176 nan 0.0100 0.0005
140 0.8925 nan 0.0100 0.0004
160 0.8732 nan 0.0100 0.0003
180 0.8577 nan 0.0100 0.0003
200 0.8446 nan 0.0100 0.0001
220 0.8327 nan 0.0100 0.0001
240 0.8221 nan 0.0100 0.0001
260 0.8131 nan 0.0100 -0.0000
280 0.8040 nan 0.0100 0.0001
300 0.7962 nan 0.0100 -0.0001
320 0.7893 nan 0.0100 -0.0001
340 0.7827 nan 0.0100 -0.0001
360 0.7765 nan 0.0100 -0.0001
380 0.7702 nan 0.0100 -0.0000
400 0.7649 nan 0.0100 -0.0001
420 0.7602 nan 0.0100 0.0000
440 0.7550 nan 0.0100 -0.0001
460 0.7503 nan 0.0100 -0.0000
480 0.7461 nan 0.0100 0.0000
500 0.7417 nan 0.0100 0.0001
520 0.7378 nan 0.0100 -0.0001
540 0.7338 nan 0.0100 -0.0001
560 0.7301 nan 0.0100 -0.0001
580 0.7268 nan 0.0100 -0.0001
600 0.7229 nan 0.0100 -0.0001
620 0.7191 nan 0.0100 -0.0001
640 0.7147 nan 0.0100 -0.0001
660 0.7110 nan 0.0100 -0.0001
680 0.7076 nan 0.0100 -0.0001
700 0.7046 nan 0.0100 -0.0002
720 0.7012 nan 0.0100 -0.0001
740 0.6984 nan 0.0100 -0.0002
760 0.6957 nan 0.0100 -0.0002
780 0.6928 nan 0.0100 -0.0000
800 0.6895 nan 0.0100 -0.0000
820 0.6867 nan 0.0100 -0.0001
840 0.6837 nan 0.0100 -0.0000
860 0.6811 nan 0.0100 -0.0001
880 0.6783 nan 0.0100 -0.0001
900 0.6758 nan 0.0100 0.0000
920 0.6731 nan 0.0100 -0.0002
940 0.6703 nan 0.0100 -0.0001
960 0.6680 nan 0.0100 -0.0000
980 0.6653 nan 0.0100 -0.0000
1000 0.6627 nan 0.0100 -0.0001
1020 0.6604 nan 0.0100 -0.0000
1040 0.6575 nan 0.0100 -0.0001
1060 0.6547 nan 0.0100 -0.0002
1080 0.6525 nan 0.0100 -0.0001
1100 0.6502 nan 0.0100 -0.0002
- Fold09.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2756 nan 0.1000 0.0281
2 1.2300 nan 0.1000 0.0228
3 1.1925 nan 0.1000 0.0177
4 1.1652 nan 0.1000 0.0149
5 1.1397 nan 0.1000 0.0133
6 1.1184 nan 0.1000 0.0110
7 1.1011 nan 0.1000 0.0065
8 1.0831 nan 0.1000 0.0088
9 1.0657 nan 0.1000 0.0072
10 1.0513 nan 0.1000 0.0057
20 0.9664 nan 0.1000 0.0029
40 0.8901 nan 0.1000 0.0008
60 0.8533 nan 0.1000 0.0004
80 0.8304 nan 0.1000 -0.0009
100 0.8132 nan 0.1000 -0.0004
120 0.7998 nan 0.1000 -0.0012
140 0.7881 nan 0.1000 -0.0008
160 0.7806 nan 0.1000 -0.0004
180 0.7722 nan 0.1000 -0.0005
200 0.7681 nan 0.1000 -0.0005
220 0.7599 nan 0.1000 -0.0008
240 0.7543 nan 0.1000 -0.0006
260 0.7496 nan 0.1000 -0.0005
280 0.7449 nan 0.1000 -0.0007
300 0.7403 nan 0.1000 -0.0003
320 0.7379 nan 0.1000 -0.0006
340 0.7333 nan 0.1000 -0.0008
360 0.7281 nan 0.1000 -0.0009
380 0.7247 nan 0.1000 -0.0012
400 0.7231 nan 0.1000 -0.0010
420 0.7191 nan 0.1000 -0.0006
440 0.7165 nan 0.1000 -0.0004
460 0.7139 nan 0.1000 -0.0010
480 0.7111 nan 0.1000 -0.0009
500 0.7092 nan 0.1000 -0.0011
520 0.7069 nan 0.1000 -0.0016
540 0.7051 nan 0.1000 -0.0004
560 0.7016 nan 0.1000 -0.0005
580 0.6988 nan 0.1000 -0.0006
600 0.6971 nan 0.1000 -0.0007
620 0.6956 nan 0.1000 -0.0010
640 0.6946 nan 0.1000 -0.0005
660 0.6921 nan 0.1000 -0.0006
680 0.6911 nan 0.1000 -0.0006
700 0.6886 nan 0.1000 -0.0004
720 0.6865 nan 0.1000 -0.0012
740 0.6855 nan 0.1000 -0.0010
760 0.6833 nan 0.1000 -0.0016
780 0.6824 nan 0.1000 -0.0008
800 0.6799 nan 0.1000 -0.0007
820 0.6788 nan 0.1000 -0.0005
840 0.6767 nan 0.1000 -0.0007
860 0.6747 nan 0.1000 -0.0007
880 0.6731 nan 0.1000 -0.0006
900 0.6720 nan 0.1000 -0.0002
920 0.6714 nan 0.1000 -0.0002
940 0.6688 nan 0.1000 -0.0005
960 0.6667 nan 0.1000 -0.0007
980 0.6658 nan 0.1000 -0.0005
1000 0.6641 nan 0.1000 -0.0011
1020 0.6623 nan 0.1000 -0.0006
1040 0.6624 nan 0.1000 -0.0010
1060 0.6606 nan 0.1000 -0.0011
1080 0.6596 nan 0.1000 -0.0010
1100 0.6578 nan 0.1000 -0.0007
- Fold09.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2637 nan 0.1000 0.0339
2 1.2079 nan 0.1000 0.0273
3 1.1624 nan 0.1000 0.0222
4 1.1235 nan 0.1000 0.0203
5 1.0902 nan 0.1000 0.0164
6 1.0573 nan 0.1000 0.0143
7 1.0327 nan 0.1000 0.0114
8 1.0105 nan 0.1000 0.0097
9 0.9927 nan 0.1000 0.0082
10 0.9795 nan 0.1000 0.0050
20 0.8816 nan 0.1000 0.0017
40 0.8087 nan 0.1000 -0.0003
60 0.7690 nan 0.1000 -0.0011
80 0.7451 nan 0.1000 -0.0012
100 0.7243 nan 0.1000 -0.0011
120 0.7070 nan 0.1000 -0.0003
140 0.6926 nan 0.1000 -0.0014
160 0.6737 nan 0.1000 -0.0009
180 0.6624 nan 0.1000 -0.0010
200 0.6532 nan 0.1000 -0.0015
220 0.6411 nan 0.1000 -0.0010
240 0.6293 nan 0.1000 -0.0003
260 0.6176 nan 0.1000 -0.0012
280 0.6098 nan 0.1000 -0.0006
300 0.6031 nan 0.1000 -0.0006
320 0.5938 nan 0.1000 -0.0009
340 0.5872 nan 0.1000 -0.0010
360 0.5812 nan 0.1000 -0.0006
380 0.5741 nan 0.1000 -0.0010
400 0.5667 nan 0.1000 -0.0011
420 0.5595 nan 0.1000 -0.0010
440 0.5527 nan 0.1000 -0.0009
460 0.5444 nan 0.1000 -0.0009
480 0.5372 nan 0.1000 -0.0009
500 0.5309 nan 0.1000 -0.0009
520 0.5252 nan 0.1000 -0.0013
540 0.5204 nan 0.1000 -0.0017
560 0.5139 nan 0.1000 -0.0008
580 0.5071 nan 0.1000 -0.0010
600 0.5026 nan 0.1000 -0.0011
620 0.4980 nan 0.1000 -0.0008
640 0.4940 nan 0.1000 -0.0008
660 0.4914 nan 0.1000 -0.0005
680 0.4854 nan 0.1000 -0.0007
700 0.4794 nan 0.1000 -0.0008
720 0.4727 nan 0.1000 -0.0006
740 0.4677 nan 0.1000 -0.0011
760 0.4640 nan 0.1000 -0.0005
780 0.4606 nan 0.1000 -0.0010
800 0.4564 nan 0.1000 -0.0014
820 0.4530 nan 0.1000 -0.0005
840 0.4487 nan 0.1000 -0.0006
860 0.4441 nan 0.1000 -0.0005
880 0.4416 nan 0.1000 -0.0012
900 0.4368 nan 0.1000 -0.0008
920 0.4339 nan 0.1000 -0.0013
940 0.4304 nan 0.1000 -0.0007
960 0.4264 nan 0.1000 -0.0005
980 0.4219 nan 0.1000 -0.0008
1000 0.4188 nan 0.1000 -0.0012
1020 0.4147 nan 0.1000 -0.0005
1040 0.4121 nan 0.1000 -0.0008
1060 0.4093 nan 0.1000 -0.0006
1080 0.4064 nan 0.1000 -0.0010
1100 0.4041 nan 0.1000 -0.0010
- Fold09.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2582 nan 0.1000 0.0348
2 1.1946 nan 0.1000 0.0316
3 1.1389 nan 0.1000 0.0250
4 1.0964 nan 0.1000 0.0209
5 1.0651 nan 0.1000 0.0172
6 1.0350 nan 0.1000 0.0153
7 1.0051 nan 0.1000 0.0143
8 0.9834 nan 0.1000 0.0092
9 0.9642 nan 0.1000 0.0074
10 0.9475 nan 0.1000 0.0057
20 0.8479 nan 0.1000 0.0014
40 0.7631 nan 0.1000 -0.0005
60 0.7216 nan 0.1000 -0.0000
80 0.6896 nan 0.1000 -0.0008
100 0.6646 nan 0.1000 -0.0024
120 0.6419 nan 0.1000 -0.0001
140 0.6266 nan 0.1000 -0.0022
160 0.6066 nan 0.1000 -0.0008
180 0.5937 nan 0.1000 -0.0012
200 0.5812 nan 0.1000 -0.0021
220 0.5682 nan 0.1000 -0.0018
240 0.5564 nan 0.1000 -0.0009
260 0.5447 nan 0.1000 -0.0022
280 0.5302 nan 0.1000 -0.0013
300 0.5185 nan 0.1000 -0.0008
320 0.5081 nan 0.1000 -0.0006
340 0.4990 nan 0.1000 -0.0015
360 0.4889 nan 0.1000 -0.0014
380 0.4770 nan 0.1000 -0.0011
400 0.4654 nan 0.1000 -0.0005
420 0.4557 nan 0.1000 -0.0011
440 0.4449 nan 0.1000 -0.0003
460 0.4383 nan 0.1000 -0.0007
480 0.4314 nan 0.1000 -0.0014
500 0.4232 nan 0.1000 -0.0016
520 0.4165 nan 0.1000 -0.0010
540 0.4075 nan 0.1000 -0.0009
560 0.3998 nan 0.1000 -0.0007
580 0.3940 nan 0.1000 -0.0012
600 0.3883 nan 0.1000 -0.0017
620 0.3836 nan 0.1000 -0.0014
640 0.3770 nan 0.1000 -0.0014
660 0.3710 nan 0.1000 -0.0008
680 0.3652 nan 0.1000 -0.0020
700 0.3597 nan 0.1000 -0.0019
720 0.3522 nan 0.1000 -0.0012
740 0.3468 nan 0.1000 -0.0007
760 0.3424 nan 0.1000 -0.0018
780 0.3388 nan 0.1000 -0.0007
800 0.3331 nan 0.1000 -0.0013
820 0.3283 nan 0.1000 -0.0006
840 0.3245 nan 0.1000 -0.0008
860 0.3185 nan 0.1000 -0.0005
880 0.3129 nan 0.1000 -0.0007
900 0.3090 nan 0.1000 -0.0011
920 0.3052 nan 0.1000 -0.0012
940 0.3016 nan 0.1000 -0.0010
960 0.2973 nan 0.1000 -0.0014
980 0.2943 nan 0.1000 -0.0012
1000 0.2919 nan 0.1000 -0.0009
1020 0.2874 nan 0.1000 -0.0008
1040 0.2850 nan 0.1000 -0.0009
1060 0.2801 nan 0.1000 -0.0006
1080 0.2775 nan 0.1000 -0.0009
1100 0.2734 nan 0.1000 -0.0008
- Fold09.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3255 nan 0.0100 0.0030
2 1.3196 nan 0.0100 0.0029
3 1.3140 nan 0.0100 0.0029
4 1.3085 nan 0.0100 0.0028
5 1.3021 nan 0.0100 0.0027
6 1.2962 nan 0.0100 0.0028
7 1.2907 nan 0.0100 0.0024
8 1.2851 nan 0.0100 0.0026
9 1.2796 nan 0.0100 0.0024
10 1.2748 nan 0.0100 0.0025
20 1.2304 nan 0.0100 0.0021
40 1.1623 nan 0.0100 0.0014
60 1.1128 nan 0.0100 0.0009
80 1.0735 nan 0.0100 0.0008
100 1.0423 nan 0.0100 0.0007
120 1.0169 nan 0.0100 0.0004
140 0.9944 nan 0.0100 0.0005
160 0.9759 nan 0.0100 0.0004
180 0.9611 nan 0.0100 0.0003
200 0.9478 nan 0.0100 0.0003
220 0.9358 nan 0.0100 0.0002
240 0.9247 nan 0.0100 0.0001
260 0.9155 nan 0.0100 0.0002
280 0.9066 nan 0.0100 0.0001
300 0.8986 nan 0.0100 0.0002
320 0.8916 nan 0.0100 0.0001
340 0.8847 nan 0.0100 0.0001
360 0.8788 nan 0.0100 -0.0001
380 0.8733 nan 0.0100 0.0001
400 0.8678 nan 0.0100 0.0000
420 0.8629 nan 0.0100 0.0000
440 0.8588 nan 0.0100 0.0000
460 0.8543 nan 0.0100 0.0001
480 0.8501 nan 0.0100 0.0000
500 0.8463 nan 0.0100 0.0000
520 0.8423 nan 0.0100 0.0000
540 0.8387 nan 0.0100 -0.0000
560 0.8353 nan 0.0100 0.0000
580 0.8320 nan 0.0100 0.0000
600 0.8288 nan 0.0100 -0.0000
620 0.8256 nan 0.0100 -0.0000
640 0.8224 nan 0.0100 0.0000
660 0.8198 nan 0.0100 -0.0000
680 0.8169 nan 0.0100 0.0000
700 0.8144 nan 0.0100 -0.0000
720 0.8118 nan 0.0100 -0.0001
740 0.8094 nan 0.0100 0.0000
760 0.8069 nan 0.0100 -0.0001
780 0.8045 nan 0.0100 -0.0001
800 0.8025 nan 0.0100 -0.0001
820 0.8004 nan 0.0100 0.0000
840 0.7983 nan 0.0100 0.0000
860 0.7961 nan 0.0100 -0.0000
880 0.7941 nan 0.0100 -0.0001
900 0.7925 nan 0.0100 -0.0000
920 0.7908 nan 0.0100 -0.0001
940 0.7892 nan 0.0100 -0.0000
960 0.7875 nan 0.0100 0.0000
980 0.7857 nan 0.0100 -0.0000
1000 0.7839 nan 0.0100 -0.0000
1020 0.7822 nan 0.0100 -0.0001
1040 0.7807 nan 0.0100 0.0000
1060 0.7794 nan 0.0100 -0.0001
1080 0.7780 nan 0.0100 -0.0000
1100 0.7764 nan 0.0100 -0.0000
- Fold10.Rep4: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3237 nan 0.0100 0.0037
2 1.3163 nan 0.0100 0.0038
3 1.3086 nan 0.0100 0.0033
4 1.3016 nan 0.0100 0.0034
5 1.2947 nan 0.0100 0.0034
6 1.2883 nan 0.0100 0.0032
7 1.2821 nan 0.0100 0.0032
8 1.2758 nan 0.0100 0.0029
9 1.2694 nan 0.0100 0.0031
10 1.2632 nan 0.0100 0.0031
20 1.2066 nan 0.0100 0.0024
40 1.1199 nan 0.0100 0.0019
60 1.0548 nan 0.0100 0.0014
80 1.0045 nan 0.0100 0.0010
100 0.9664 nan 0.0100 0.0007
120 0.9379 nan 0.0100 0.0004
140 0.9144 nan 0.0100 0.0003
160 0.8944 nan 0.0100 0.0003
180 0.8779 nan 0.0100 0.0001
200 0.8635 nan 0.0100 0.0001
220 0.8515 nan 0.0100 0.0003
240 0.8405 nan 0.0100 0.0001
260 0.8311 nan 0.0100 0.0001
280 0.8228 nan 0.0100 0.0001
300 0.8151 nan 0.0100 0.0002
320 0.8078 nan 0.0100 0.0001
340 0.8018 nan 0.0100 -0.0000
360 0.7953 nan 0.0100 0.0000
380 0.7893 nan 0.0100 -0.0001
400 0.7839 nan 0.0100 0.0000
420 0.7794 nan 0.0100 0.0000
440 0.7749 nan 0.0100 0.0000
460 0.7708 nan 0.0100 0.0000
480 0.7668 nan 0.0100 0.0000
500 0.7623 nan 0.0100 0.0000
520 0.7586 nan 0.0100 -0.0000
540 0.7555 nan 0.0100 -0.0001
560 0.7520 nan 0.0100 -0.0001
580 0.7487 nan 0.0100 -0.0001
600 0.7453 nan 0.0100 -0.0001
620 0.7423 nan 0.0100 -0.0000
640 0.7395 nan 0.0100 -0.0001
660 0.7367 nan 0.0100 -0.0000
680 0.7338 nan 0.0100 -0.0000
700 0.7307 nan 0.0100 -0.0000
720 0.7283 nan 0.0100 -0.0000
740 0.7260 nan 0.0100 -0.0001
760 0.7235 nan 0.0100 0.0000
780 0.7210 nan 0.0100 -0.0000
800 0.7189 nan 0.0100 -0.0001
820 0.7161 nan 0.0100 -0.0001
840 0.7139 nan 0.0100 -0.0000
860 0.7115 nan 0.0100 -0.0001
880 0.7093 nan 0.0100 -0.0001
900 0.7070 nan 0.0100 -0.0000
920 0.7052 nan 0.0100 -0.0001
940 0.7032 nan 0.0100 -0.0001
960 0.7014 nan 0.0100 -0.0002
980 0.6991 nan 0.0100 -0.0000
1000 0.6970 nan 0.0100 -0.0001
1020 0.6950 nan 0.0100 -0.0001
1040 0.6932 nan 0.0100 -0.0001
1060 0.6914 nan 0.0100 -0.0001
1080 0.6895 nan 0.0100 -0.0000
1100 0.6873 nan 0.0100 -0.0000
- Fold10.Rep4: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3228 nan 0.0100 0.0039
2 1.3151 nan 0.0100 0.0037
3 1.3071 nan 0.0100 0.0036
4 1.2993 nan 0.0100 0.0038
5 1.2918 nan 0.0100 0.0038
6 1.2845 nan 0.0100 0.0036
7 1.2767 nan 0.0100 0.0037
8 1.2693 nan 0.0100 0.0036
9 1.2621 nan 0.0100 0.0032
10 1.2553 nan 0.0100 0.0033
20 1.1941 nan 0.0100 0.0026
40 1.0964 nan 0.0100 0.0018
60 1.0256 nan 0.0100 0.0015
80 0.9721 nan 0.0100 0.0010
100 0.9312 nan 0.0100 0.0008
120 0.9004 nan 0.0100 0.0003
140 0.8752 nan 0.0100 0.0003
160 0.8552 nan 0.0100 0.0003
180 0.8376 nan 0.0100 0.0001
200 0.8227 nan 0.0100 0.0002
220 0.8098 nan 0.0100 0.0000
240 0.7987 nan 0.0100 0.0000
260 0.7895 nan 0.0100 -0.0001
280 0.7807 nan 0.0100 0.0001
300 0.7728 nan 0.0100 0.0001
320 0.7645 nan 0.0100 0.0000
340 0.7572 nan 0.0100 0.0001
360 0.7507 nan 0.0100 0.0000
380 0.7445 nan 0.0100 0.0000
400 0.7385 nan 0.0100 -0.0000
420 0.7332 nan 0.0100 -0.0002
440 0.7283 nan 0.0100 -0.0002
460 0.7230 nan 0.0100 -0.0001
480 0.7186 nan 0.0100 0.0000
500 0.7141 nan 0.0100 -0.0001
520 0.7100 nan 0.0100 -0.0001
540 0.7058 nan 0.0100 -0.0001
560 0.7014 nan 0.0100 -0.0002
580 0.6977 nan 0.0100 -0.0002
600 0.6936 nan 0.0100 -0.0001
620 0.6899 nan 0.0100 -0.0001
640 0.6868 nan 0.0100 -0.0003
660 0.6834 nan 0.0100 -0.0001
680 0.6800 nan 0.0100 -0.0000
700 0.6770 nan 0.0100 -0.0001
720 0.6743 nan 0.0100 -0.0000
740 0.6714 nan 0.0100 -0.0001
760 0.6685 nan 0.0100 -0.0001
780 0.6655 nan 0.0100 -0.0001
800 0.6626 nan 0.0100 -0.0000
820 0.6602 nan 0.0100 -0.0001
840 0.6576 nan 0.0100 -0.0001
860 0.6555 nan 0.0100 -0.0002
880 0.6526 nan 0.0100 -0.0000
900 0.6494 nan 0.0100 -0.0001
920 0.6471 nan 0.0100 -0.0000
940 0.6443 nan 0.0100 -0.0001
960 0.6414 nan 0.0100 -0.0001
980 0.6389 nan 0.0100 -0.0001
1000 0.6367 nan 0.0100 -0.0002
1020 0.6341 nan 0.0100 -0.0000
1040 0.6314 nan 0.0100 -0.0001
1060 0.6292 nan 0.0100 -0.0002
1080 0.6266 nan 0.0100 -0.0002
1100 0.6243 nan 0.0100 -0.0001
- Fold10.Rep4: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2716 nan 0.1000 0.0281
2 1.2294 nan 0.1000 0.0219
3 1.1935 nan 0.1000 0.0196
4 1.1601 nan 0.1000 0.0165
5 1.1395 nan 0.1000 0.0089
6 1.1142 nan 0.1000 0.0113
7 1.0904 nan 0.1000 0.0108
8 1.0721 nan 0.1000 0.0092
9 1.0563 nan 0.1000 0.0072
10 1.0415 nan 0.1000 0.0069
20 0.9500 nan 0.1000 -0.0001
40 0.8681 nan 0.1000 0.0007
60 0.8285 nan 0.1000 0.0002
80 0.8029 nan 0.1000 0.0001
100 0.7844 nan 0.1000 -0.0004
120 0.7710 nan 0.1000 -0.0001
140 0.7578 nan 0.1000 -0.0005
160 0.7483 nan 0.1000 -0.0003
180 0.7407 nan 0.1000 -0.0012
200 0.7331 nan 0.1000 -0.0002
220 0.7280 nan 0.1000 -0.0017
240 0.7210 nan 0.1000 -0.0004
260 0.7150 nan 0.1000 -0.0016
280 0.7108 nan 0.1000 -0.0007
300 0.7074 nan 0.1000 -0.0004
320 0.7043 nan 0.1000 -0.0006
340 0.6986 nan 0.1000 -0.0005
360 0.6965 nan 0.1000 -0.0003
380 0.6932 nan 0.1000 -0.0010
400 0.6903 nan 0.1000 -0.0006
420 0.6874 nan 0.1000 -0.0007
440 0.6857 nan 0.1000 -0.0009
460 0.6824 nan 0.1000 -0.0006
480 0.6798 nan 0.1000 -0.0004
500 0.6777 nan 0.1000 -0.0005
520 0.6760 nan 0.1000 -0.0011
540 0.6737 nan 0.1000 -0.0014
560 0.6712 nan 0.1000 -0.0002
580 0.6691 nan 0.1000 -0.0013
600 0.6670 nan 0.1000 -0.0008
620 0.6661 nan 0.1000 -0.0005
640 0.6630 nan 0.1000 -0.0009
660 0.6606 nan 0.1000 -0.0013
680 0.6580 nan 0.1000 -0.0002
700 0.6564 nan 0.1000 -0.0009
720 0.6528 nan 0.1000 -0.0006
740 0.6525 nan 0.1000 -0.0008
760 0.6505 nan 0.1000 -0.0003
780 0.6488 nan 0.1000 -0.0008
800 0.6468 nan 0.1000 -0.0008
820 0.6455 nan 0.1000 -0.0007
840 0.6448 nan 0.1000 -0.0008
860 0.6425 nan 0.1000 -0.0004
880 0.6416 nan 0.1000 -0.0008
900 0.6396 nan 0.1000 -0.0005
920 0.6375 nan 0.1000 -0.0005
940 0.6365 nan 0.1000 -0.0008
960 0.6352 nan 0.1000 -0.0007
980 0.6334 nan 0.1000 -0.0003
1000 0.6314 nan 0.1000 -0.0016
1020 0.6300 nan 0.1000 -0.0005
1040 0.6288 nan 0.1000 -0.0007
1060 0.6278 nan 0.1000 -0.0006
1080 0.6262 nan 0.1000 -0.0010
1100 0.6252 nan 0.1000 -0.0004
- Fold10.Rep4: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2598 nan 0.1000 0.0333
2 1.2017 nan 0.1000 0.0279
3 1.1512 nan 0.1000 0.0219
4 1.1105 nan 0.1000 0.0186
5 1.0789 nan 0.1000 0.0141
6 1.0488 nan 0.1000 0.0136
7 1.0253 nan 0.1000 0.0125
8 1.0058 nan 0.1000 0.0106
9 0.9819 nan 0.1000 0.0106
10 0.9648 nan 0.1000 0.0073
20 0.8646 nan 0.1000 0.0029
40 0.7906 nan 0.1000 -0.0014
60 0.7509 nan 0.1000 -0.0001
80 0.7198 nan 0.1000 -0.0013
100 0.6989 nan 0.1000 -0.0005
120 0.6802 nan 0.1000 -0.0003
140 0.6670 nan 0.1000 -0.0007
160 0.6513 nan 0.1000 -0.0012
180 0.6354 nan 0.1000 -0.0017
200 0.6252 nan 0.1000 -0.0011
220 0.6124 nan 0.1000 -0.0012
240 0.5983 nan 0.1000 -0.0012
260 0.5892 nan 0.1000 -0.0002
280 0.5807 nan 0.1000 -0.0012
300 0.5746 nan 0.1000 -0.0015
320 0.5673 nan 0.1000 -0.0011
340 0.5615 nan 0.1000 -0.0010
360 0.5538 nan 0.1000 -0.0011
380 0.5475 nan 0.1000 -0.0010
400 0.5398 nan 0.1000 -0.0012
420 0.5338 nan 0.1000 -0.0008
440 0.5279 nan 0.1000 -0.0012
460 0.5214 nan 0.1000 -0.0004
480 0.5166 nan 0.1000 -0.0010
500 0.5092 nan 0.1000 -0.0007
520 0.5027 nan 0.1000 -0.0008
540 0.4985 nan 0.1000 -0.0008
560 0.4933 nan 0.1000 -0.0007
580 0.4889 nan 0.1000 -0.0005
600 0.4862 nan 0.1000 -0.0013
620 0.4788 nan 0.1000 -0.0007
640 0.4745 nan 0.1000 -0.0013
660 0.4712 nan 0.1000 -0.0011
680 0.4656 nan 0.1000 -0.0006
700 0.4618 nan 0.1000 -0.0003
720 0.4555 nan 0.1000 -0.0010
740 0.4511 nan 0.1000 -0.0008
760 0.4478 nan 0.1000 -0.0007
780 0.4440 nan 0.1000 -0.0010
800 0.4401 nan 0.1000 -0.0003
820 0.4360 nan 0.1000 -0.0010
840 0.4308 nan 0.1000 -0.0008
860 0.4274 nan 0.1000 -0.0004
880 0.4225 nan 0.1000 -0.0006
900 0.4209 nan 0.1000 -0.0010
920 0.4175 nan 0.1000 -0.0012
940 0.4151 nan 0.1000 -0.0009
960 0.4123 nan 0.1000 -0.0013
980 0.4105 nan 0.1000 -0.0003
1000 0.4059 nan 0.1000 -0.0007
1020 0.4028 nan 0.1000 -0.0010
1040 0.3997 nan 0.1000 -0.0002
1060 0.3974 nan 0.1000 -0.0005
1080 0.3951 nan 0.1000 -0.0006
1100 0.3931 nan 0.1000 -0.0014
- Fold10.Rep4: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2473 nan 0.1000 0.0355
2 1.1886 nan 0.1000 0.0298
3 1.1347 nan 0.1000 0.0263
4 1.0888 nan 0.1000 0.0219
5 1.0539 nan 0.1000 0.0174
6 1.0218 nan 0.1000 0.0151
7 0.9910 nan 0.1000 0.0149
8 0.9683 nan 0.1000 0.0103
9 0.9519 nan 0.1000 0.0068
10 0.9331 nan 0.1000 0.0065
20 0.8269 nan 0.1000 0.0001
40 0.7405 nan 0.1000 -0.0005
60 0.7023 nan 0.1000 -0.0012
80 0.6708 nan 0.1000 -0.0015
100 0.6460 nan 0.1000 -0.0028
120 0.6195 nan 0.1000 -0.0011
140 0.5964 nan 0.1000 -0.0008
160 0.5763 nan 0.1000 -0.0013
180 0.5626 nan 0.1000 -0.0014
200 0.5486 nan 0.1000 -0.0011
220 0.5355 nan 0.1000 -0.0004
240 0.5208 nan 0.1000 -0.0021
260 0.5067 nan 0.1000 -0.0012
280 0.4925 nan 0.1000 -0.0011
300 0.4804 nan 0.1000 -0.0016
320 0.4686 nan 0.1000 -0.0009
340 0.4572 nan 0.1000 -0.0003
360 0.4487 nan 0.1000 -0.0010
380 0.4407 nan 0.1000 -0.0004
400 0.4268 nan 0.1000 -0.0013
420 0.4190 nan 0.1000 -0.0004
440 0.4101 nan 0.1000 -0.0008
460 0.4024 nan 0.1000 -0.0018
480 0.3930 nan 0.1000 -0.0002
500 0.3834 nan 0.1000 -0.0010
520 0.3787 nan 0.1000 -0.0006
540 0.3728 nan 0.1000 -0.0013
560 0.3650 nan 0.1000 -0.0007
580 0.3589 nan 0.1000 -0.0004
600 0.3536 nan 0.1000 -0.0010
620 0.3481 nan 0.1000 -0.0013
640 0.3438 nan 0.1000 -0.0008
660 0.3391 nan 0.1000 -0.0007
680 0.3350 nan 0.1000 -0.0007
700 0.3301 nan 0.1000 -0.0004
720 0.3239 nan 0.1000 -0.0019
740 0.3197 nan 0.1000 -0.0009
760 0.3155 nan 0.1000 -0.0012
780 0.3105 nan 0.1000 -0.0006
800 0.3057 nan 0.1000 -0.0002
820 0.3018 nan 0.1000 -0.0013
840 0.2988 nan 0.1000 -0.0007
860 0.2938 nan 0.1000 -0.0009
880 0.2899 nan 0.1000 -0.0007
900 0.2862 nan 0.1000 -0.0012
920 0.2822 nan 0.1000 -0.0014
940 0.2789 nan 0.1000 -0.0011
960 0.2758 nan 0.1000 -0.0009
980 0.2723 nan 0.1000 -0.0009
1000 0.2692 nan 0.1000 -0.0004
1020 0.2662 nan 0.1000 -0.0010
1040 0.2632 nan 0.1000 -0.0010
1060 0.2597 nan 0.1000 -0.0008
1080 0.2562 nan 0.1000 -0.0004
1100 0.2540 nan 0.1000 -0.0010
- Fold10.Rep4: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3260 nan 0.0100 0.0030
2 1.3195 nan 0.0100 0.0029
3 1.3135 nan 0.0100 0.0029
4 1.3076 nan 0.0100 0.0027
5 1.3020 nan 0.0100 0.0027
6 1.2967 nan 0.0100 0.0026
7 1.2917 nan 0.0100 0.0026
8 1.2867 nan 0.0100 0.0026
9 1.2817 nan 0.0100 0.0026
10 1.2767 nan 0.0100 0.0025
20 1.2316 nan 0.0100 0.0021
40 1.1623 nan 0.0100 0.0015
60 1.1120 nan 0.0100 0.0010
80 1.0744 nan 0.0100 0.0008
100 1.0445 nan 0.0100 0.0006
120 1.0204 nan 0.0100 0.0005
140 1.0010 nan 0.0100 0.0003
160 0.9835 nan 0.0100 0.0003
180 0.9685 nan 0.0100 0.0003
200 0.9560 nan 0.0100 0.0002
220 0.9446 nan 0.0100 0.0002
240 0.9345 nan 0.0100 0.0001
260 0.9254 nan 0.0100 0.0001
280 0.9169 nan 0.0100 0.0001
300 0.9092 nan 0.0100 0.0001
320 0.9022 nan 0.0100 0.0000
340 0.8964 nan 0.0100 0.0000
360 0.8902 nan 0.0100 0.0000
380 0.8847 nan 0.0100 -0.0001
400 0.8794 nan 0.0100 -0.0000
420 0.8744 nan 0.0100 -0.0000
440 0.8702 nan 0.0100 0.0001
460 0.8654 nan 0.0100 0.0000
480 0.8614 nan 0.0100 0.0000
500 0.8573 nan 0.0100 0.0001
520 0.8534 nan 0.0100 0.0001
540 0.8500 nan 0.0100 0.0000
560 0.8464 nan 0.0100 0.0000
580 0.8429 nan 0.0100 -0.0000
600 0.8397 nan 0.0100 0.0000
620 0.8366 nan 0.0100 -0.0000
640 0.8335 nan 0.0100 -0.0000
660 0.8306 nan 0.0100 0.0000
680 0.8280 nan 0.0100 -0.0000
700 0.8252 nan 0.0100 -0.0000
720 0.8227 nan 0.0100 0.0000
740 0.8202 nan 0.0100 0.0000
760 0.8177 nan 0.0100 -0.0001
780 0.8153 nan 0.0100 -0.0000
800 0.8129 nan 0.0100 -0.0001
820 0.8107 nan 0.0100 -0.0000
840 0.8085 nan 0.0100 -0.0000
860 0.8064 nan 0.0100 0.0000
880 0.8045 nan 0.0100 -0.0000
900 0.8025 nan 0.0100 -0.0000
920 0.8009 nan 0.0100 -0.0000
940 0.7994 nan 0.0100 -0.0001
960 0.7975 nan 0.0100 0.0000
980 0.7958 nan 0.0100 -0.0001
1000 0.7941 nan 0.0100 -0.0000
1020 0.7927 nan 0.0100 -0.0000
1040 0.7911 nan 0.0100 -0.0000
1060 0.7895 nan 0.0100 -0.0001
1080 0.7880 nan 0.0100 -0.0001
1100 0.7866 nan 0.0100 0.0000
- Fold01.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0038
2 1.3172 nan 0.0100 0.0036
3 1.3100 nan 0.0100 0.0035
4 1.3040 nan 0.0100 0.0029
5 1.2970 nan 0.0100 0.0034
6 1.2903 nan 0.0100 0.0033
7 1.2837 nan 0.0100 0.0033
8 1.2773 nan 0.0100 0.0033
9 1.2708 nan 0.0100 0.0030
10 1.2643 nan 0.0100 0.0031
20 1.2077 nan 0.0100 0.0026
40 1.1197 nan 0.0100 0.0018
60 1.0541 nan 0.0100 0.0013
80 1.0056 nan 0.0100 0.0008
100 0.9700 nan 0.0100 0.0007
120 0.9416 nan 0.0100 0.0003
140 0.9186 nan 0.0100 0.0005
160 0.9001 nan 0.0100 0.0003
180 0.8853 nan 0.0100 0.0001
200 0.8719 nan 0.0100 0.0001
220 0.8597 nan 0.0100 0.0001
240 0.8496 nan 0.0100 0.0001
260 0.8407 nan 0.0100 0.0001
280 0.8328 nan 0.0100 0.0000
300 0.8256 nan 0.0100 0.0001
320 0.8187 nan 0.0100 0.0002
340 0.8123 nan 0.0100 0.0000
360 0.8055 nan 0.0100 0.0000
380 0.7996 nan 0.0100 0.0001
400 0.7939 nan 0.0100 0.0001
420 0.7887 nan 0.0100 0.0000
440 0.7841 nan 0.0100 -0.0000
460 0.7797 nan 0.0100 0.0000
480 0.7752 nan 0.0100 -0.0000
500 0.7715 nan 0.0100 -0.0001
520 0.7673 nan 0.0100 -0.0001
540 0.7638 nan 0.0100 -0.0001
560 0.7601 nan 0.0100 -0.0000
580 0.7565 nan 0.0100 -0.0001
600 0.7532 nan 0.0100 -0.0001
620 0.7493 nan 0.0100 -0.0000
640 0.7465 nan 0.0100 -0.0001
660 0.7434 nan 0.0100 -0.0001
680 0.7403 nan 0.0100 -0.0000
700 0.7373 nan 0.0100 -0.0000
720 0.7346 nan 0.0100 -0.0000
740 0.7322 nan 0.0100 -0.0001
760 0.7298 nan 0.0100 -0.0000
780 0.7274 nan 0.0100 -0.0001
800 0.7246 nan 0.0100 -0.0002
820 0.7225 nan 0.0100 -0.0001
840 0.7195 nan 0.0100 -0.0001
860 0.7170 nan 0.0100 -0.0001
880 0.7148 nan 0.0100 -0.0001
900 0.7127 nan 0.0100 -0.0001
920 0.7107 nan 0.0100 0.0000
940 0.7089 nan 0.0100 -0.0001
960 0.7065 nan 0.0100 -0.0000
980 0.7046 nan 0.0100 -0.0001
1000 0.7026 nan 0.0100 -0.0001
1020 0.7005 nan 0.0100 -0.0001
1040 0.6985 nan 0.0100 -0.0002
1060 0.6963 nan 0.0100 -0.0001
1080 0.6942 nan 0.0100 -0.0001
1100 0.6923 nan 0.0100 -0.0001
- Fold01.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0040
2 1.3166 nan 0.0100 0.0039
3 1.3084 nan 0.0100 0.0039
4 1.3009 nan 0.0100 0.0038
5 1.2936 nan 0.0100 0.0036
6 1.2860 nan 0.0100 0.0036
7 1.2786 nan 0.0100 0.0034
8 1.2715 nan 0.0100 0.0032
9 1.2642 nan 0.0100 0.0035
10 1.2575 nan 0.0100 0.0033
20 1.1953 nan 0.0100 0.0027
40 1.0993 nan 0.0100 0.0020
60 1.0290 nan 0.0100 0.0014
80 0.9763 nan 0.0100 0.0009
100 0.9373 nan 0.0100 0.0007
120 0.9070 nan 0.0100 0.0005
140 0.8839 nan 0.0100 0.0003
160 0.8639 nan 0.0100 0.0004
180 0.8477 nan 0.0100 0.0003
200 0.8340 nan 0.0100 0.0001
220 0.8216 nan 0.0100 0.0001
240 0.8105 nan 0.0100 0.0001
260 0.7998 nan 0.0100 0.0001
280 0.7913 nan 0.0100 0.0000
300 0.7833 nan 0.0100 -0.0001
320 0.7754 nan 0.0100 -0.0001
340 0.7678 nan 0.0100 -0.0001
360 0.7610 nan 0.0100 -0.0001
380 0.7557 nan 0.0100 -0.0000
400 0.7505 nan 0.0100 -0.0001
420 0.7456 nan 0.0100 -0.0000
440 0.7404 nan 0.0100 -0.0001
460 0.7354 nan 0.0100 0.0000
480 0.7307 nan 0.0100 -0.0001
500 0.7260 nan 0.0100 -0.0001
520 0.7216 nan 0.0100 -0.0001
540 0.7176 nan 0.0100 -0.0000
560 0.7134 nan 0.0100 0.0000
580 0.7099 nan 0.0100 0.0000
600 0.7064 nan 0.0100 -0.0001
620 0.7028 nan 0.0100 -0.0001
640 0.6991 nan 0.0100 -0.0000
660 0.6958 nan 0.0100 -0.0000
680 0.6918 nan 0.0100 -0.0001
700 0.6885 nan 0.0100 -0.0001
720 0.6850 nan 0.0100 -0.0000
740 0.6819 nan 0.0100 -0.0002
760 0.6786 nan 0.0100 -0.0000
780 0.6753 nan 0.0100 -0.0001
800 0.6721 nan 0.0100 -0.0000
820 0.6690 nan 0.0100 -0.0001
840 0.6657 nan 0.0100 -0.0001
860 0.6626 nan 0.0100 -0.0001
880 0.6600 nan 0.0100 -0.0001
900 0.6572 nan 0.0100 -0.0000
920 0.6540 nan 0.0100 -0.0001
940 0.6517 nan 0.0100 -0.0001
960 0.6487 nan 0.0100 -0.0001
980 0.6457 nan 0.0100 -0.0001
1000 0.6430 nan 0.0100 -0.0001
1020 0.6404 nan 0.0100 -0.0002
1040 0.6378 nan 0.0100 -0.0001
1060 0.6353 nan 0.0100 -0.0002
1080 0.6323 nan 0.0100 -0.0001
1100 0.6300 nan 0.0100 -0.0001
- Fold01.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2785 nan 0.1000 0.0276
2 1.2274 nan 0.1000 0.0239
3 1.1877 nan 0.1000 0.0185
4 1.1595 nan 0.1000 0.0161
5 1.1335 nan 0.1000 0.0136
6 1.1099 nan 0.1000 0.0110
7 1.0916 nan 0.1000 0.0085
8 1.0737 nan 0.1000 0.0065
9 1.0553 nan 0.1000 0.0076
10 1.0411 nan 0.1000 0.0062
20 0.9542 nan 0.1000 0.0017
40 0.8788 nan 0.1000 0.0005
60 0.8385 nan 0.1000 -0.0001
80 0.8127 nan 0.1000 -0.0010
100 0.7938 nan 0.1000 -0.0005
120 0.7812 nan 0.1000 -0.0011
140 0.7706 nan 0.1000 -0.0008
160 0.7632 nan 0.1000 -0.0018
180 0.7548 nan 0.1000 -0.0011
200 0.7484 nan 0.1000 -0.0004
220 0.7429 nan 0.1000 -0.0013
240 0.7363 nan 0.1000 -0.0008
260 0.7320 nan 0.1000 -0.0004
280 0.7267 nan 0.1000 -0.0009
300 0.7230 nan 0.1000 -0.0014
320 0.7187 nan 0.1000 -0.0007
340 0.7147 nan 0.1000 -0.0010
360 0.7122 nan 0.1000 -0.0002
380 0.7094 nan 0.1000 -0.0008
400 0.7059 nan 0.1000 -0.0006
420 0.7019 nan 0.1000 -0.0007
440 0.6990 nan 0.1000 -0.0006
460 0.6960 nan 0.1000 -0.0005
480 0.6914 nan 0.1000 -0.0004
500 0.6895 nan 0.1000 -0.0010
520 0.6867 nan 0.1000 -0.0010
540 0.6854 nan 0.1000 -0.0009
560 0.6818 nan 0.1000 -0.0013
580 0.6777 nan 0.1000 -0.0007
600 0.6753 nan 0.1000 -0.0006
620 0.6730 nan 0.1000 -0.0006
640 0.6717 nan 0.1000 -0.0007
660 0.6686 nan 0.1000 -0.0007
680 0.6663 nan 0.1000 -0.0006
700 0.6649 nan 0.1000 -0.0006
720 0.6627 nan 0.1000 -0.0008
740 0.6620 nan 0.1000 -0.0008
760 0.6606 nan 0.1000 -0.0005
780 0.6585 nan 0.1000 -0.0008
800 0.6570 nan 0.1000 -0.0004
820 0.6557 nan 0.1000 -0.0006
840 0.6539 nan 0.1000 -0.0008
860 0.6524 nan 0.1000 -0.0006
880 0.6509 nan 0.1000 -0.0014
900 0.6492 nan 0.1000 -0.0005
920 0.6482 nan 0.1000 -0.0007
940 0.6466 nan 0.1000 -0.0013
960 0.6461 nan 0.1000 -0.0005
980 0.6440 nan 0.1000 -0.0003
1000 0.6412 nan 0.1000 -0.0007
1020 0.6397 nan 0.1000 -0.0008
1040 0.6379 nan 0.1000 -0.0003
1060 0.6365 nan 0.1000 -0.0008
1080 0.6363 nan 0.1000 -0.0006
1100 0.6341 nan 0.1000 -0.0012
- Fold01.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2587 nan 0.1000 0.0351
2 1.1984 nan 0.1000 0.0292
3 1.1488 nan 0.1000 0.0209
4 1.1040 nan 0.1000 0.0201
5 1.0740 nan 0.1000 0.0160
6 1.0465 nan 0.1000 0.0089
7 1.0200 nan 0.1000 0.0113
8 0.9957 nan 0.1000 0.0107
9 0.9771 nan 0.1000 0.0080
10 0.9626 nan 0.1000 0.0074
20 0.8749 nan 0.1000 0.0009
40 0.7982 nan 0.1000 0.0000
60 0.7625 nan 0.1000 -0.0007
80 0.7337 nan 0.1000 -0.0004
100 0.7115 nan 0.1000 -0.0007
120 0.6930 nan 0.1000 0.0002
140 0.6770 nan 0.1000 -0.0003
160 0.6600 nan 0.1000 -0.0006
180 0.6476 nan 0.1000 -0.0008
200 0.6358 nan 0.1000 -0.0010
220 0.6243 nan 0.1000 -0.0006
240 0.6140 nan 0.1000 -0.0017
260 0.6015 nan 0.1000 -0.0010
280 0.5912 nan 0.1000 -0.0008
300 0.5843 nan 0.1000 -0.0011
320 0.5728 nan 0.1000 -0.0010
340 0.5649 nan 0.1000 -0.0008
360 0.5573 nan 0.1000 -0.0006
380 0.5495 nan 0.1000 -0.0006
400 0.5418 nan 0.1000 -0.0009
420 0.5343 nan 0.1000 -0.0013
440 0.5246 nan 0.1000 -0.0014
460 0.5169 nan 0.1000 -0.0012
480 0.5104 nan 0.1000 -0.0015
500 0.5058 nan 0.1000 -0.0004
520 0.5021 nan 0.1000 -0.0016
540 0.4955 nan 0.1000 -0.0008
560 0.4906 nan 0.1000 -0.0009
580 0.4856 nan 0.1000 -0.0014
600 0.4778 nan 0.1000 -0.0008
620 0.4710 nan 0.1000 -0.0016
640 0.4671 nan 0.1000 -0.0009
660 0.4631 nan 0.1000 -0.0012
680 0.4580 nan 0.1000 -0.0012
700 0.4543 nan 0.1000 -0.0004
720 0.4509 nan 0.1000 -0.0008
740 0.4478 nan 0.1000 -0.0007
760 0.4452 nan 0.1000 -0.0014
780 0.4403 nan 0.1000 -0.0007
800 0.4356 nan 0.1000 -0.0010
820 0.4320 nan 0.1000 -0.0008
840 0.4284 nan 0.1000 -0.0011
860 0.4255 nan 0.1000 -0.0008
880 0.4205 nan 0.1000 -0.0004
900 0.4175 nan 0.1000 -0.0005
920 0.4125 nan 0.1000 -0.0012
940 0.4078 nan 0.1000 -0.0005
960 0.4042 nan 0.1000 -0.0015
980 0.4013 nan 0.1000 -0.0004
1000 0.3984 nan 0.1000 -0.0004
1020 0.3945 nan 0.1000 -0.0005
1040 0.3908 nan 0.1000 -0.0008
1060 0.3892 nan 0.1000 -0.0009
1080 0.3865 nan 0.1000 -0.0010
1100 0.3836 nan 0.1000 -0.0014
- Fold01.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold01.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2533 nan 0.1000 0.0410
2 1.1869 nan 0.1000 0.0295
3 1.1342 nan 0.1000 0.0252
4 1.0888 nan 0.1000 0.0223
5 1.0517 nan 0.1000 0.0172
6 1.0204 nan 0.1000 0.0155
7 0.9906 nan 0.1000 0.0123
8 0.9647 nan 0.1000 0.0110
9 0.9445 nan 0.1000 0.0083
10 0.9260 nan 0.1000 0.0074
20 0.8262 nan 0.1000 0.0024
40 0.7448 nan 0.1000 0.0008
60 0.7079 nan 0.1000 -0.0004
80 0.6712 nan 0.1000 -0.0008
100 0.6445 nan 0.1000 -0.0007
120 0.6183 nan 0.1000 -0.0017
140 0.5923 nan 0.1000 -0.0010
160 0.5744 nan 0.1000 -0.0019
180 0.5587 nan 0.1000 -0.0017
200 0.5411 nan 0.1000 -0.0013
220 0.5281 nan 0.1000 -0.0012
240 0.5137 nan 0.1000 -0.0016
260 0.5004 nan 0.1000 -0.0012
280 0.4852 nan 0.1000 -0.0019
300 0.4747 nan 0.1000 -0.0009
320 0.4664 nan 0.1000 -0.0016
340 0.4552 nan 0.1000 -0.0014
360 0.4439 nan 0.1000 -0.0012
380 0.4348 nan 0.1000 -0.0016
400 0.4239 nan 0.1000 -0.0011
420 0.4167 nan 0.1000 -0.0015
440 0.4088 nan 0.1000 -0.0002
460 0.3992 nan 0.1000 -0.0017
480 0.3898 nan 0.1000 -0.0003
500 0.3837 nan 0.1000 -0.0009
520 0.3770 nan 0.1000 -0.0009
540 0.3701 nan 0.1000 -0.0012
560 0.3642 nan 0.1000 -0.0014
580 0.3587 nan 0.1000 -0.0004
600 0.3520 nan 0.1000 -0.0004
620 0.3462 nan 0.1000 -0.0009
640 0.3411 nan 0.1000 -0.0009
660 0.3350 nan 0.1000 -0.0004
680 0.3277 nan 0.1000 -0.0007
700 0.3246 nan 0.1000 -0.0011
720 0.3193 nan 0.1000 -0.0008
740 0.3153 nan 0.1000 -0.0010
760 0.3114 nan 0.1000 -0.0005
780 0.3067 nan 0.1000 -0.0009
800 0.3016 nan 0.1000 -0.0005
820 0.2979 nan 0.1000 -0.0010
840 0.2947 nan 0.1000 -0.0011
860 0.2891 nan 0.1000 -0.0005
880 0.2838 nan 0.1000 -0.0008
900 0.2808 nan 0.1000 -0.0008
920 0.2770 nan 0.1000 -0.0006
940 0.2733 nan 0.1000 -0.0005
960 0.2696 nan 0.1000 -0.0013
980 0.2667 nan 0.1000 -0.0003
1000 0.2636 nan 0.1000 -0.0004
1020 0.2601 nan 0.1000 -0.0004
1040 0.2575 nan 0.1000 -0.0007
1060 0.2543 nan 0.1000 -0.0005
1080 0.2516 nan 0.1000 -0.0006
1100 0.2485 nan 0.1000 -0.0003
- Fold01.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3248 nan 0.0100 0.0032
2 1.3190 nan 0.0100 0.0029
3 1.3134 nan 0.0100 0.0029
4 1.3076 nan 0.0100 0.0028
5 1.3019 nan 0.0100 0.0029
6 1.2958 nan 0.0100 0.0028
7 1.2897 nan 0.0100 0.0028
8 1.2840 nan 0.0100 0.0028
9 1.2789 nan 0.0100 0.0025
10 1.2735 nan 0.0100 0.0026
20 1.2264 nan 0.0100 0.0021
40 1.1545 nan 0.0100 0.0015
60 1.1046 nan 0.0100 0.0011
80 1.0667 nan 0.0100 0.0008
100 1.0370 nan 0.0100 0.0006
120 1.0129 nan 0.0100 0.0005
140 0.9922 nan 0.0100 0.0004
160 0.9751 nan 0.0100 0.0003
180 0.9609 nan 0.0100 0.0002
200 0.9478 nan 0.0100 0.0002
220 0.9359 nan 0.0100 0.0001
240 0.9256 nan 0.0100 0.0001
260 0.9168 nan 0.0100 0.0001
280 0.9085 nan 0.0100 -0.0001
300 0.9011 nan 0.0100 0.0001
320 0.8940 nan 0.0100 0.0001
340 0.8876 nan 0.0100 0.0001
360 0.8816 nan 0.0100 0.0001
380 0.8759 nan 0.0100 0.0000
400 0.8708 nan 0.0100 0.0001
420 0.8660 nan 0.0100 0.0001
440 0.8614 nan 0.0100 0.0000
460 0.8573 nan 0.0100 0.0001
480 0.8534 nan 0.0100 0.0001
500 0.8497 nan 0.0100 0.0000
520 0.8463 nan 0.0100 0.0000
540 0.8427 nan 0.0100 -0.0000
560 0.8392 nan 0.0100 -0.0000
580 0.8360 nan 0.0100 -0.0000
600 0.8328 nan 0.0100 -0.0000
620 0.8300 nan 0.0100 0.0000
640 0.8271 nan 0.0100 0.0000
660 0.8245 nan 0.0100 -0.0000
680 0.8220 nan 0.0100 -0.0000
700 0.8196 nan 0.0100 -0.0000
720 0.8174 nan 0.0100 -0.0000
740 0.8152 nan 0.0100 -0.0000
760 0.8132 nan 0.0100 -0.0000
780 0.8109 nan 0.0100 -0.0000
800 0.8089 nan 0.0100 -0.0000
820 0.8070 nan 0.0100 -0.0000
840 0.8052 nan 0.0100 -0.0000
860 0.8032 nan 0.0100 -0.0000
880 0.8014 nan 0.0100 -0.0000
900 0.7995 nan 0.0100 -0.0000
920 0.7979 nan 0.0100 -0.0001
940 0.7964 nan 0.0100 -0.0001
960 0.7949 nan 0.0100 -0.0002
980 0.7933 nan 0.0100 -0.0000
1000 0.7918 nan 0.0100 -0.0000
1020 0.7905 nan 0.0100 -0.0000
1040 0.7890 nan 0.0100 -0.0000
1060 0.7877 nan 0.0100 -0.0001
1080 0.7863 nan 0.0100 -0.0000
1100 0.7851 nan 0.0100 -0.0000
- Fold02.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3232 nan 0.0100 0.0036
2 1.3161 nan 0.0100 0.0035
3 1.3088 nan 0.0100 0.0034
4 1.3013 nan 0.0100 0.0036
5 1.2943 nan 0.0100 0.0034
6 1.2872 nan 0.0100 0.0033
7 1.2805 nan 0.0100 0.0031
8 1.2739 nan 0.0100 0.0032
9 1.2677 nan 0.0100 0.0029
10 1.2615 nan 0.0100 0.0031
20 1.2055 nan 0.0100 0.0023
40 1.1172 nan 0.0100 0.0019
60 1.0519 nan 0.0100 0.0014
80 1.0032 nan 0.0100 0.0010
100 0.9662 nan 0.0100 0.0006
120 0.9369 nan 0.0100 0.0005
140 0.9143 nan 0.0100 0.0003
160 0.8959 nan 0.0100 0.0004
180 0.8806 nan 0.0100 0.0001
200 0.8676 nan 0.0100 0.0002
220 0.8558 nan 0.0100 0.0001
240 0.8454 nan 0.0100 0.0003
260 0.8373 nan 0.0100 -0.0001
280 0.8291 nan 0.0100 0.0001
300 0.8220 nan 0.0100 -0.0001
320 0.8155 nan 0.0100 -0.0001
340 0.8092 nan 0.0100 -0.0000
360 0.8035 nan 0.0100 0.0000
380 0.7976 nan 0.0100 0.0001
400 0.7929 nan 0.0100 -0.0001
420 0.7875 nan 0.0100 -0.0000
440 0.7822 nan 0.0100 -0.0000
460 0.7783 nan 0.0100 -0.0000
480 0.7742 nan 0.0100 -0.0001
500 0.7699 nan 0.0100 -0.0001
520 0.7663 nan 0.0100 -0.0000
540 0.7631 nan 0.0100 -0.0000
560 0.7597 nan 0.0100 -0.0000
580 0.7566 nan 0.0100 -0.0001
600 0.7538 nan 0.0100 -0.0001
620 0.7510 nan 0.0100 -0.0001
640 0.7481 nan 0.0100 -0.0001
660 0.7454 nan 0.0100 -0.0001
680 0.7426 nan 0.0100 -0.0000
700 0.7399 nan 0.0100 -0.0000
720 0.7374 nan 0.0100 -0.0000
740 0.7349 nan 0.0100 -0.0001
760 0.7326 nan 0.0100 -0.0002
780 0.7299 nan 0.0100 -0.0001
800 0.7271 nan 0.0100 -0.0000
820 0.7249 nan 0.0100 -0.0001
840 0.7230 nan 0.0100 -0.0001
860 0.7212 nan 0.0100 -0.0001
880 0.7190 nan 0.0100 -0.0001
900 0.7166 nan 0.0100 -0.0001
920 0.7145 nan 0.0100 -0.0001
940 0.7126 nan 0.0100 -0.0001
960 0.7107 nan 0.0100 0.0000
980 0.7087 nan 0.0100 -0.0001
1000 0.7065 nan 0.0100 -0.0001
1020 0.7044 nan 0.0100 -0.0001
1040 0.7025 nan 0.0100 -0.0001
1060 0.7009 nan 0.0100 -0.0001
1080 0.6990 nan 0.0100 0.0000
1100 0.6974 nan 0.0100 -0.0001
- Fold02.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3237 nan 0.0100 0.0035
2 1.3157 nan 0.0100 0.0040
3 1.3082 nan 0.0100 0.0036
4 1.3006 nan 0.0100 0.0034
5 1.2932 nan 0.0100 0.0035
6 1.2863 nan 0.0100 0.0036
7 1.2787 nan 0.0100 0.0036
8 1.2713 nan 0.0100 0.0037
9 1.2646 nan 0.0100 0.0034
10 1.2574 nan 0.0100 0.0033
20 1.1954 nan 0.0100 0.0027
40 1.0982 nan 0.0100 0.0021
60 1.0277 nan 0.0100 0.0015
80 0.9752 nan 0.0100 0.0011
100 0.9347 nan 0.0100 0.0007
120 0.9050 nan 0.0100 0.0005
140 0.8804 nan 0.0100 0.0003
160 0.8618 nan 0.0100 0.0005
180 0.8453 nan 0.0100 0.0001
200 0.8304 nan 0.0100 0.0002
220 0.8176 nan 0.0100 0.0001
240 0.8071 nan 0.0100 -0.0001
260 0.7972 nan 0.0100 0.0001
280 0.7886 nan 0.0100 0.0002
300 0.7811 nan 0.0100 0.0000
320 0.7739 nan 0.0100 0.0001
340 0.7666 nan 0.0100 -0.0000
360 0.7599 nan 0.0100 0.0001
380 0.7539 nan 0.0100 0.0001
400 0.7489 nan 0.0100 -0.0000
420 0.7444 nan 0.0100 -0.0000
440 0.7400 nan 0.0100 -0.0001
460 0.7354 nan 0.0100 -0.0002
480 0.7313 nan 0.0100 -0.0002
500 0.7269 nan 0.0100 -0.0000
520 0.7230 nan 0.0100 -0.0001
540 0.7193 nan 0.0100 -0.0001
560 0.7156 nan 0.0100 -0.0001
580 0.7117 nan 0.0100 -0.0001
600 0.7078 nan 0.0100 -0.0001
620 0.7047 nan 0.0100 -0.0001
640 0.7010 nan 0.0100 -0.0001
660 0.6972 nan 0.0100 -0.0001
680 0.6936 nan 0.0100 -0.0000
700 0.6902 nan 0.0100 -0.0002
720 0.6870 nan 0.0100 -0.0001
740 0.6838 nan 0.0100 -0.0001
760 0.6807 nan 0.0100 -0.0001
780 0.6778 nan 0.0100 0.0000
800 0.6748 nan 0.0100 -0.0001
820 0.6721 nan 0.0100 -0.0001
840 0.6696 nan 0.0100 -0.0001
860 0.6668 nan 0.0100 -0.0000
880 0.6639 nan 0.0100 -0.0001
900 0.6612 nan 0.0100 -0.0001
920 0.6588 nan 0.0100 -0.0000
940 0.6565 nan 0.0100 -0.0000
960 0.6535 nan 0.0100 -0.0001
980 0.6507 nan 0.0100 -0.0000
1000 0.6484 nan 0.0100 -0.0000
1020 0.6458 nan 0.0100 -0.0001
1040 0.6433 nan 0.0100 -0.0001
1060 0.6413 nan 0.0100 -0.0001
1080 0.6387 nan 0.0100 -0.0001
1100 0.6359 nan 0.0100 -0.0001
- Fold02.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2741 nan 0.1000 0.0291
2 1.2222 nan 0.1000 0.0249
3 1.1852 nan 0.1000 0.0191
4 1.1559 nan 0.1000 0.0160
5 1.1268 nan 0.1000 0.0145
6 1.1038 nan 0.1000 0.0124
7 1.0860 nan 0.1000 0.0067
8 1.0630 nan 0.1000 0.0091
9 1.0484 nan 0.1000 0.0074
10 1.0332 nan 0.1000 0.0050
20 0.9444 nan 0.1000 0.0005
40 0.8694 nan 0.1000 0.0004
60 0.8321 nan 0.1000 -0.0002
80 0.8101 nan 0.1000 -0.0005
100 0.7953 nan 0.1000 -0.0007
120 0.7820 nan 0.1000 0.0002
140 0.7723 nan 0.1000 -0.0007
160 0.7614 nan 0.1000 -0.0014
180 0.7537 nan 0.1000 -0.0014
200 0.7468 nan 0.1000 -0.0003
220 0.7422 nan 0.1000 -0.0010
240 0.7360 nan 0.1000 -0.0007
260 0.7317 nan 0.1000 -0.0005
280 0.7272 nan 0.1000 -0.0006
300 0.7242 nan 0.1000 -0.0005
320 0.7203 nan 0.1000 -0.0010
340 0.7182 nan 0.1000 -0.0004
360 0.7147 nan 0.1000 -0.0008
380 0.7114 nan 0.1000 -0.0005
400 0.7077 nan 0.1000 -0.0010
420 0.7051 nan 0.1000 -0.0018
440 0.7017 nan 0.1000 -0.0015
460 0.6984 nan 0.1000 -0.0007
480 0.6964 nan 0.1000 -0.0006
500 0.6938 nan 0.1000 -0.0010
520 0.6909 nan 0.1000 -0.0024
540 0.6890 nan 0.1000 -0.0008
560 0.6868 nan 0.1000 -0.0009
580 0.6835 nan 0.1000 -0.0005
600 0.6816 nan 0.1000 -0.0005
620 0.6794 nan 0.1000 -0.0004
640 0.6771 nan 0.1000 -0.0009
660 0.6759 nan 0.1000 -0.0026
680 0.6751 nan 0.1000 -0.0002
700 0.6734 nan 0.1000 -0.0011
720 0.6706 nan 0.1000 -0.0011
740 0.6695 nan 0.1000 -0.0013
760 0.6674 nan 0.1000 -0.0006
780 0.6658 nan 0.1000 -0.0005
800 0.6640 nan 0.1000 -0.0006
820 0.6628 nan 0.1000 -0.0005
840 0.6615 nan 0.1000 -0.0006
860 0.6594 nan 0.1000 -0.0004
880 0.6578 nan 0.1000 -0.0008
900 0.6568 nan 0.1000 -0.0004
920 0.6560 nan 0.1000 -0.0006
940 0.6551 nan 0.1000 -0.0005
960 0.6543 nan 0.1000 -0.0007
980 0.6524 nan 0.1000 -0.0004
1000 0.6503 nan 0.1000 -0.0010
1020 0.6485 nan 0.1000 -0.0006
1040 0.6466 nan 0.1000 -0.0008
1060 0.6459 nan 0.1000 -0.0012
1080 0.6448 nan 0.1000 -0.0009
1100 0.6434 nan 0.1000 -0.0011
- Fold02.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2573 nan 0.1000 0.0352
2 1.1959 nan 0.1000 0.0268
3 1.1452 nan 0.1000 0.0212
4 1.1047 nan 0.1000 0.0194
5 1.0672 nan 0.1000 0.0162
6 1.0419 nan 0.1000 0.0128
7 1.0149 nan 0.1000 0.0113
8 0.9962 nan 0.1000 0.0066
9 0.9758 nan 0.1000 0.0090
10 0.9628 nan 0.1000 0.0053
20 0.8647 nan 0.1000 0.0019
40 0.7868 nan 0.1000 0.0014
60 0.7510 nan 0.1000 0.0002
80 0.7246 nan 0.1000 -0.0008
100 0.7024 nan 0.1000 -0.0005
120 0.6853 nan 0.1000 -0.0002
140 0.6692 nan 0.1000 -0.0012
160 0.6521 nan 0.1000 -0.0007
180 0.6400 nan 0.1000 -0.0005
200 0.6296 nan 0.1000 -0.0007
220 0.6201 nan 0.1000 -0.0001
240 0.6104 nan 0.1000 -0.0014
260 0.6009 nan 0.1000 -0.0012
280 0.5925 nan 0.1000 -0.0012
300 0.5831 nan 0.1000 -0.0010
320 0.5766 nan 0.1000 -0.0007
340 0.5692 nan 0.1000 -0.0002
360 0.5618 nan 0.1000 -0.0006
380 0.5534 nan 0.1000 -0.0017
400 0.5450 nan 0.1000 -0.0016
420 0.5381 nan 0.1000 -0.0009
440 0.5287 nan 0.1000 -0.0003
460 0.5210 nan 0.1000 -0.0012
480 0.5143 nan 0.1000 -0.0001
500 0.5074 nan 0.1000 -0.0010
520 0.5013 nan 0.1000 -0.0012
540 0.4949 nan 0.1000 -0.0012
560 0.4910 nan 0.1000 -0.0010
580 0.4854 nan 0.1000 -0.0007
600 0.4781 nan 0.1000 -0.0004
620 0.4729 nan 0.1000 -0.0009
640 0.4683 nan 0.1000 -0.0013
660 0.4640 nan 0.1000 -0.0010
680 0.4592 nan 0.1000 -0.0009
700 0.4533 nan 0.1000 -0.0011
720 0.4484 nan 0.1000 -0.0013
740 0.4416 nan 0.1000 -0.0010
760 0.4386 nan 0.1000 -0.0009
780 0.4344 nan 0.1000 -0.0009
800 0.4313 nan 0.1000 -0.0004
820 0.4278 nan 0.1000 -0.0011
840 0.4233 nan 0.1000 -0.0003
860 0.4196 nan 0.1000 -0.0007
880 0.4136 nan 0.1000 -0.0008
900 0.4093 nan 0.1000 -0.0008
920 0.4060 nan 0.1000 -0.0013
940 0.4029 nan 0.1000 -0.0005
960 0.3991 nan 0.1000 -0.0006
980 0.3959 nan 0.1000 -0.0006
1000 0.3916 nan 0.1000 -0.0010
1020 0.3888 nan 0.1000 -0.0011
1040 0.3852 nan 0.1000 -0.0009
1060 0.3825 nan 0.1000 -0.0007
1080 0.3786 nan 0.1000 -0.0007
1100 0.3766 nan 0.1000 -0.0005
- Fold02.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold02.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2540 nan 0.1000 0.0404
2 1.1898 nan 0.1000 0.0310
3 1.1307 nan 0.1000 0.0264
4 1.0856 nan 0.1000 0.0225
5 1.0468 nan 0.1000 0.0164
6 1.0195 nan 0.1000 0.0114
7 0.9902 nan 0.1000 0.0138
8 0.9675 nan 0.1000 0.0115
9 0.9454 nan 0.1000 0.0100
10 0.9263 nan 0.1000 0.0093
20 0.8282 nan 0.1000 0.0028
40 0.7508 nan 0.1000 -0.0002
60 0.7060 nan 0.1000 -0.0011
80 0.6740 nan 0.1000 -0.0009
100 0.6533 nan 0.1000 -0.0002
120 0.6341 nan 0.1000 -0.0014
140 0.6123 nan 0.1000 -0.0002
160 0.5922 nan 0.1000 -0.0008
180 0.5730 nan 0.1000 -0.0012
200 0.5554 nan 0.1000 -0.0007
220 0.5409 nan 0.1000 -0.0008
240 0.5282 nan 0.1000 -0.0012
260 0.5158 nan 0.1000 -0.0003
280 0.5024 nan 0.1000 -0.0012
300 0.4931 nan 0.1000 -0.0014
320 0.4828 nan 0.1000 -0.0018
340 0.4707 nan 0.1000 -0.0008
360 0.4614 nan 0.1000 -0.0013
380 0.4514 nan 0.1000 -0.0012
400 0.4433 nan 0.1000 -0.0018
420 0.4369 nan 0.1000 -0.0008
440 0.4290 nan 0.1000 -0.0005
460 0.4200 nan 0.1000 -0.0003
480 0.4125 nan 0.1000 -0.0010
500 0.4042 nan 0.1000 -0.0005
520 0.3974 nan 0.1000 -0.0004
540 0.3902 nan 0.1000 -0.0008
560 0.3838 nan 0.1000 -0.0016
580 0.3765 nan 0.1000 -0.0005
600 0.3697 nan 0.1000 -0.0010
620 0.3655 nan 0.1000 -0.0003
640 0.3587 nan 0.1000 -0.0015
660 0.3545 nan 0.1000 -0.0014
680 0.3472 nan 0.1000 -0.0002
700 0.3420 nan 0.1000 -0.0013
720 0.3359 nan 0.1000 -0.0009
740 0.3323 nan 0.1000 -0.0016
760 0.3272 nan 0.1000 -0.0012
780 0.3241 nan 0.1000 -0.0005
800 0.3175 nan 0.1000 -0.0007
820 0.3135 nan 0.1000 -0.0009
840 0.3076 nan 0.1000 -0.0006
860 0.3039 nan 0.1000 -0.0007
880 0.2984 nan 0.1000 -0.0010
900 0.2936 nan 0.1000 -0.0010
920 0.2879 nan 0.1000 -0.0004
940 0.2839 nan 0.1000 -0.0007
960 0.2797 nan 0.1000 -0.0007
980 0.2763 nan 0.1000 -0.0006
1000 0.2723 nan 0.1000 -0.0008
1020 0.2697 nan 0.1000 -0.0010
1040 0.2660 nan 0.1000 -0.0007
1060 0.2632 nan 0.1000 -0.0006
1080 0.2610 nan 0.1000 -0.0008
1100 0.2579 nan 0.1000 -0.0007
- Fold02.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3261 nan 0.0100 0.0028
2 1.3206 nan 0.0100 0.0028
3 1.3153 nan 0.0100 0.0027
4 1.3093 nan 0.0100 0.0027
5 1.3036 nan 0.0100 0.0026
6 1.2989 nan 0.0100 0.0026
7 1.2945 nan 0.0100 0.0025
8 1.2905 nan 0.0100 0.0024
9 1.2858 nan 0.0100 0.0024
10 1.2804 nan 0.0100 0.0022
20 1.2384 nan 0.0100 0.0020
40 1.1729 nan 0.0100 0.0013
60 1.1273 nan 0.0100 0.0010
80 1.0898 nan 0.0100 0.0008
100 1.0602 nan 0.0100 0.0006
120 1.0353 nan 0.0100 0.0005
140 1.0154 nan 0.0100 0.0005
160 0.9974 nan 0.0100 0.0003
180 0.9821 nan 0.0100 0.0002
200 0.9689 nan 0.0100 0.0002
220 0.9567 nan 0.0100 0.0002
240 0.9456 nan 0.0100 0.0001
260 0.9358 nan 0.0100 0.0000
280 0.9271 nan 0.0100 -0.0001
300 0.9190 nan 0.0100 0.0002
320 0.9113 nan 0.0100 0.0001
340 0.9042 nan 0.0100 0.0000
360 0.8981 nan 0.0100 -0.0000
380 0.8923 nan 0.0100 -0.0000
400 0.8866 nan 0.0100 0.0001
420 0.8818 nan 0.0100 -0.0000
440 0.8776 nan 0.0100 0.0000
460 0.8727 nan 0.0100 0.0000
480 0.8682 nan 0.0100 0.0001
500 0.8641 nan 0.0100 -0.0000
520 0.8601 nan 0.0100 0.0001
540 0.8567 nan 0.0100 -0.0000
560 0.8532 nan 0.0100 0.0000
580 0.8502 nan 0.0100 0.0000
600 0.8466 nan 0.0100 0.0000
620 0.8440 nan 0.0100 0.0000
640 0.8410 nan 0.0100 -0.0001
660 0.8382 nan 0.0100 -0.0001
680 0.8355 nan 0.0100 -0.0001
700 0.8329 nan 0.0100 -0.0001
720 0.8302 nan 0.0100 -0.0000
740 0.8277 nan 0.0100 -0.0000
760 0.8251 nan 0.0100 0.0000
780 0.8229 nan 0.0100 -0.0000
800 0.8205 nan 0.0100 -0.0000
820 0.8184 nan 0.0100 -0.0001
840 0.8162 nan 0.0100 -0.0001
860 0.8147 nan 0.0100 -0.0002
880 0.8127 nan 0.0100 -0.0001
900 0.8111 nan 0.0100 -0.0001
920 0.8092 nan 0.0100 0.0000
940 0.8076 nan 0.0100 -0.0000
960 0.8062 nan 0.0100 -0.0001
980 0.8046 nan 0.0100 -0.0000
1000 0.8029 nan 0.0100 -0.0000
1020 0.8013 nan 0.0100 -0.0001
1040 0.7996 nan 0.0100 -0.0001
1060 0.7982 nan 0.0100 0.0000
1080 0.7968 nan 0.0100 -0.0000
1100 0.7956 nan 0.0100 -0.0001
- Fold03.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3249 nan 0.0100 0.0036
2 1.3180 nan 0.0100 0.0035
3 1.3107 nan 0.0100 0.0035
4 1.3031 nan 0.0100 0.0032
5 1.2963 nan 0.0100 0.0036
6 1.2894 nan 0.0100 0.0031
7 1.2827 nan 0.0100 0.0032
8 1.2762 nan 0.0100 0.0032
9 1.2701 nan 0.0100 0.0027
10 1.2638 nan 0.0100 0.0029
20 1.2079 nan 0.0100 0.0026
40 1.1237 nan 0.0100 0.0019
60 1.0619 nan 0.0100 0.0013
80 1.0148 nan 0.0100 0.0009
100 0.9771 nan 0.0100 0.0007
120 0.9489 nan 0.0100 0.0006
140 0.9262 nan 0.0100 0.0004
160 0.9072 nan 0.0100 0.0003
180 0.8917 nan 0.0100 0.0002
200 0.8786 nan 0.0100 0.0002
220 0.8662 nan 0.0100 0.0002
240 0.8553 nan 0.0100 0.0003
260 0.8463 nan 0.0100 0.0002
280 0.8378 nan 0.0100 0.0000
300 0.8305 nan 0.0100 0.0000
320 0.8227 nan 0.0100 0.0000
340 0.8160 nan 0.0100 0.0000
360 0.8097 nan 0.0100 0.0001
380 0.8044 nan 0.0100 -0.0001
400 0.7992 nan 0.0100 -0.0001
420 0.7940 nan 0.0100 -0.0001
440 0.7894 nan 0.0100 -0.0000
460 0.7848 nan 0.0100 0.0000
480 0.7807 nan 0.0100 -0.0000
500 0.7769 nan 0.0100 -0.0001
520 0.7733 nan 0.0100 -0.0001
540 0.7697 nan 0.0100 0.0000
560 0.7662 nan 0.0100 -0.0002
580 0.7630 nan 0.0100 -0.0000
600 0.7600 nan 0.0100 -0.0000
620 0.7568 nan 0.0100 0.0000
640 0.7540 nan 0.0100 -0.0000
660 0.7510 nan 0.0100 -0.0001
680 0.7480 nan 0.0100 -0.0001
700 0.7451 nan 0.0100 -0.0000
720 0.7424 nan 0.0100 -0.0001
740 0.7400 nan 0.0100 -0.0000
760 0.7371 nan 0.0100 -0.0000
780 0.7350 nan 0.0100 -0.0001
800 0.7325 nan 0.0100 -0.0000
820 0.7299 nan 0.0100 -0.0000
840 0.7275 nan 0.0100 -0.0001
860 0.7254 nan 0.0100 -0.0001
880 0.7228 nan 0.0100 -0.0000
900 0.7207 nan 0.0100 -0.0001
920 0.7184 nan 0.0100 -0.0001
940 0.7162 nan 0.0100 -0.0001
960 0.7137 nan 0.0100 -0.0001
980 0.7117 nan 0.0100 -0.0002
1000 0.7092 nan 0.0100 -0.0001
1020 0.7068 nan 0.0100 -0.0001
1040 0.7048 nan 0.0100 -0.0001
1060 0.7030 nan 0.0100 -0.0001
1080 0.7014 nan 0.0100 -0.0001
1100 0.6995 nan 0.0100 -0.0001
- Fold03.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3236 nan 0.0100 0.0035
2 1.3155 nan 0.0100 0.0039
3 1.3079 nan 0.0100 0.0038
4 1.3006 nan 0.0100 0.0037
5 1.2929 nan 0.0100 0.0037
6 1.2855 nan 0.0100 0.0034
7 1.2779 nan 0.0100 0.0037
8 1.2707 nan 0.0100 0.0033
9 1.2638 nan 0.0100 0.0033
10 1.2567 nan 0.0100 0.0033
20 1.1948 nan 0.0100 0.0026
40 1.0992 nan 0.0100 0.0019
60 1.0289 nan 0.0100 0.0014
80 0.9787 nan 0.0100 0.0010
100 0.9394 nan 0.0100 0.0008
120 0.9115 nan 0.0100 0.0003
140 0.8879 nan 0.0100 0.0006
160 0.8681 nan 0.0100 0.0002
180 0.8523 nan 0.0100 0.0001
200 0.8383 nan 0.0100 0.0002
220 0.8255 nan 0.0100 0.0003
240 0.8143 nan 0.0100 -0.0000
260 0.8036 nan 0.0100 -0.0000
280 0.7943 nan 0.0100 0.0002
300 0.7862 nan 0.0100 -0.0000
320 0.7788 nan 0.0100 0.0000
340 0.7718 nan 0.0100 -0.0001
360 0.7659 nan 0.0100 -0.0001
380 0.7597 nan 0.0100 -0.0002
400 0.7540 nan 0.0100 0.0000
420 0.7483 nan 0.0100 -0.0001
440 0.7432 nan 0.0100 0.0000
460 0.7386 nan 0.0100 -0.0001
480 0.7344 nan 0.0100 -0.0001
500 0.7301 nan 0.0100 -0.0003
520 0.7260 nan 0.0100 -0.0001
540 0.7223 nan 0.0100 -0.0002
560 0.7184 nan 0.0100 -0.0002
580 0.7142 nan 0.0100 -0.0001
600 0.7106 nan 0.0100 -0.0001
620 0.7070 nan 0.0100 -0.0001
640 0.7034 nan 0.0100 -0.0001
660 0.6995 nan 0.0100 0.0001
680 0.6959 nan 0.0100 -0.0002
700 0.6923 nan 0.0100 -0.0001
720 0.6890 nan 0.0100 -0.0000
740 0.6855 nan 0.0100 -0.0001
760 0.6824 nan 0.0100 -0.0002
780 0.6792 nan 0.0100 -0.0001
800 0.6762 nan 0.0100 -0.0001
820 0.6734 nan 0.0100 -0.0001
840 0.6699 nan 0.0100 -0.0000
860 0.6670 nan 0.0100 -0.0001
880 0.6645 nan 0.0100 -0.0001
900 0.6614 nan 0.0100 -0.0000
920 0.6587 nan 0.0100 -0.0001
940 0.6562 nan 0.0100 -0.0001
960 0.6532 nan 0.0100 -0.0001
980 0.6513 nan 0.0100 -0.0001
1000 0.6490 nan 0.0100 -0.0001
1020 0.6462 nan 0.0100 -0.0001
1040 0.6437 nan 0.0100 -0.0002
1060 0.6408 nan 0.0100 -0.0001
1080 0.6382 nan 0.0100 -0.0002
1100 0.6357 nan 0.0100 -0.0001
- Fold03.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2831 nan 0.1000 0.0271
2 1.2389 nan 0.1000 0.0223
3 1.1998 nan 0.1000 0.0192
4 1.1725 nan 0.1000 0.0143
5 1.1441 nan 0.1000 0.0131
6 1.1211 nan 0.1000 0.0106
7 1.1030 nan 0.1000 0.0084
8 1.0884 nan 0.1000 0.0070
9 1.0697 nan 0.1000 0.0081
10 1.0576 nan 0.1000 0.0051
20 0.9660 nan 0.1000 0.0030
40 0.8868 nan 0.1000 -0.0000
60 0.8445 nan 0.1000 0.0007
80 0.8203 nan 0.1000 -0.0004
100 0.8010 nan 0.1000 -0.0002
120 0.7892 nan 0.1000 -0.0006
140 0.7786 nan 0.1000 -0.0005
160 0.7718 nan 0.1000 -0.0009
180 0.7665 nan 0.1000 -0.0018
200 0.7600 nan 0.1000 -0.0007
220 0.7536 nan 0.1000 -0.0007
240 0.7485 nan 0.1000 -0.0002
260 0.7450 nan 0.1000 -0.0004
280 0.7393 nan 0.1000 -0.0008
300 0.7364 nan 0.1000 -0.0007
320 0.7321 nan 0.1000 -0.0004
340 0.7277 nan 0.1000 -0.0013
360 0.7240 nan 0.1000 -0.0011
380 0.7217 nan 0.1000 -0.0013
400 0.7168 nan 0.1000 -0.0016
420 0.7154 nan 0.1000 -0.0007
440 0.7128 nan 0.1000 -0.0008
460 0.7122 nan 0.1000 -0.0005
480 0.7093 nan 0.1000 -0.0011
500 0.7062 nan 0.1000 -0.0008
520 0.7043 nan 0.1000 -0.0010
540 0.7021 nan 0.1000 -0.0005
560 0.7003 nan 0.1000 -0.0006
580 0.6998 nan 0.1000 -0.0004
600 0.6979 nan 0.1000 -0.0013
620 0.6953 nan 0.1000 -0.0010
640 0.6933 nan 0.1000 -0.0007
660 0.6907 nan 0.1000 -0.0007
680 0.6902 nan 0.1000 -0.0010
700 0.6883 nan 0.1000 -0.0004
720 0.6868 nan 0.1000 -0.0006
740 0.6849 nan 0.1000 -0.0003
760 0.6828 nan 0.1000 -0.0003
780 0.6807 nan 0.1000 -0.0006
800 0.6776 nan 0.1000 -0.0006
820 0.6760 nan 0.1000 -0.0010
840 0.6751 nan 0.1000 -0.0008
860 0.6733 nan 0.1000 -0.0007
880 0.6720 nan 0.1000 -0.0005
900 0.6712 nan 0.1000 -0.0007
920 0.6718 nan 0.1000 -0.0012
940 0.6693 nan 0.1000 -0.0011
960 0.6689 nan 0.1000 -0.0010
980 0.6682 nan 0.1000 -0.0006
1000 0.6664 nan 0.1000 -0.0009
1020 0.6654 nan 0.1000 -0.0005
1040 0.6630 nan 0.1000 -0.0005
1060 0.6620 nan 0.1000 -0.0010
1080 0.6610 nan 0.1000 -0.0006
1100 0.6606 nan 0.1000 -0.0007
- Fold03.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2632 nan 0.1000 0.0347
2 1.2124 nan 0.1000 0.0250
3 1.1619 nan 0.1000 0.0225
4 1.1188 nan 0.1000 0.0219
5 1.0840 nan 0.1000 0.0177
6 1.0517 nan 0.1000 0.0153
7 1.0278 nan 0.1000 0.0116
8 1.0087 nan 0.1000 0.0097
9 0.9889 nan 0.1000 0.0100
10 0.9717 nan 0.1000 0.0079
20 0.8776 nan 0.1000 0.0023
40 0.8003 nan 0.1000 -0.0002
60 0.7632 nan 0.1000 -0.0017
80 0.7398 nan 0.1000 0.0001
100 0.7165 nan 0.1000 -0.0008
120 0.6999 nan 0.1000 -0.0014
140 0.6825 nan 0.1000 -0.0015
160 0.6690 nan 0.1000 -0.0013
180 0.6571 nan 0.1000 -0.0005
200 0.6477 nan 0.1000 -0.0011
220 0.6390 nan 0.1000 -0.0008
240 0.6234 nan 0.1000 -0.0010
260 0.6115 nan 0.1000 -0.0016
280 0.6029 nan 0.1000 -0.0009
300 0.5920 nan 0.1000 -0.0009
320 0.5823 nan 0.1000 -0.0012
340 0.5733 nan 0.1000 -0.0006
360 0.5645 nan 0.1000 -0.0010
380 0.5570 nan 0.1000 -0.0001
400 0.5502 nan 0.1000 -0.0003
420 0.5440 nan 0.1000 -0.0007
440 0.5376 nan 0.1000 -0.0006
460 0.5304 nan 0.1000 -0.0006
480 0.5244 nan 0.1000 -0.0004
500 0.5164 nan 0.1000 -0.0007
520 0.5083 nan 0.1000 -0.0007
540 0.5019 nan 0.1000 -0.0008
560 0.4978 nan 0.1000 -0.0007
580 0.4927 nan 0.1000 -0.0006
600 0.4891 nan 0.1000 -0.0014
620 0.4846 nan 0.1000 -0.0007
640 0.4793 nan 0.1000 -0.0015
660 0.4748 nan 0.1000 -0.0016
680 0.4704 nan 0.1000 -0.0009
700 0.4648 nan 0.1000 -0.0006
720 0.4622 nan 0.1000 -0.0008
740 0.4568 nan 0.1000 -0.0008
760 0.4534 nan 0.1000 -0.0006
780 0.4484 nan 0.1000 -0.0008
800 0.4455 nan 0.1000 -0.0009
820 0.4410 nan 0.1000 -0.0003
840 0.4370 nan 0.1000 -0.0004
860 0.4334 nan 0.1000 -0.0007
880 0.4302 nan 0.1000 -0.0007
900 0.4278 nan 0.1000 -0.0007
920 0.4235 nan 0.1000 -0.0008
940 0.4202 nan 0.1000 -0.0003
960 0.4175 nan 0.1000 -0.0010
980 0.4145 nan 0.1000 -0.0012
1000 0.4102 nan 0.1000 -0.0005
1020 0.4067 nan 0.1000 -0.0005
1040 0.4050 nan 0.1000 -0.0013
1060 0.4014 nan 0.1000 -0.0006
1080 0.3984 nan 0.1000 -0.0004
1100 0.3947 nan 0.1000 -0.0010
- Fold03.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold03.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2541 nan 0.1000 0.0371
2 1.1949 nan 0.1000 0.0295
3 1.1434 nan 0.1000 0.0244
4 1.0964 nan 0.1000 0.0221
5 1.0615 nan 0.1000 0.0181
6 1.0256 nan 0.1000 0.0165
7 0.9941 nan 0.1000 0.0133
8 0.9683 nan 0.1000 0.0119
9 0.9492 nan 0.1000 0.0090
10 0.9388 nan 0.1000 0.0024
20 0.8398 nan 0.1000 0.0012
40 0.7561 nan 0.1000 -0.0009
60 0.7115 nan 0.1000 -0.0014
80 0.6755 nan 0.1000 -0.0013
100 0.6483 nan 0.1000 -0.0007
120 0.6250 nan 0.1000 -0.0026
140 0.6062 nan 0.1000 -0.0009
160 0.5914 nan 0.1000 -0.0017
180 0.5743 nan 0.1000 -0.0014
200 0.5579 nan 0.1000 -0.0009
220 0.5395 nan 0.1000 -0.0008
240 0.5269 nan 0.1000 -0.0012
260 0.5130 nan 0.1000 -0.0009
280 0.4993 nan 0.1000 -0.0015
300 0.4876 nan 0.1000 -0.0013
320 0.4770 nan 0.1000 -0.0009
340 0.4690 nan 0.1000 -0.0011
360 0.4610 nan 0.1000 -0.0015
380 0.4507 nan 0.1000 -0.0018
400 0.4416 nan 0.1000 -0.0010
420 0.4333 nan 0.1000 -0.0012
440 0.4237 nan 0.1000 -0.0014
460 0.4166 nan 0.1000 -0.0012
480 0.4084 nan 0.1000 -0.0010
500 0.4018 nan 0.1000 -0.0018
520 0.3943 nan 0.1000 -0.0009
540 0.3868 nan 0.1000 -0.0011
560 0.3812 nan 0.1000 -0.0004
580 0.3735 nan 0.1000 -0.0009
600 0.3682 nan 0.1000 -0.0005
620 0.3617 nan 0.1000 -0.0011
640 0.3560 nan 0.1000 -0.0009
660 0.3524 nan 0.1000 -0.0011
680 0.3459 nan 0.1000 -0.0006
700 0.3404 nan 0.1000 -0.0013
720 0.3356 nan 0.1000 -0.0008
740 0.3310 nan 0.1000 -0.0008
760 0.3251 nan 0.1000 -0.0008
780 0.3199 nan 0.1000 -0.0009
800 0.3153 nan 0.1000 -0.0008
820 0.3125 nan 0.1000 -0.0012
840 0.3076 nan 0.1000 -0.0013
860 0.3038 nan 0.1000 -0.0011
880 0.3002 nan 0.1000 -0.0009
900 0.2958 nan 0.1000 -0.0007
920 0.2927 nan 0.1000 -0.0009
940 0.2896 nan 0.1000 -0.0010
960 0.2862 nan 0.1000 -0.0005
980 0.2820 nan 0.1000 -0.0015
1000 0.2792 nan 0.1000 -0.0013
1020 0.2748 nan 0.1000 -0.0013
1040 0.2708 nan 0.1000 -0.0008
1060 0.2677 nan 0.1000 -0.0011
1080 0.2656 nan 0.1000 -0.0007
1100 0.2626 nan 0.1000 -0.0010
- Fold03.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3260 nan 0.0100 0.0032
2 1.3203 nan 0.0100 0.0030
3 1.3142 nan 0.0100 0.0030
4 1.3080 nan 0.0100 0.0029
5 1.3018 nan 0.0100 0.0028
6 1.2958 nan 0.0100 0.0027
7 1.2899 nan 0.0100 0.0027
8 1.2839 nan 0.0100 0.0027
9 1.2787 nan 0.0100 0.0027
10 1.2732 nan 0.0100 0.0026
20 1.2271 nan 0.0100 0.0022
40 1.1519 nan 0.0100 0.0015
60 1.1019 nan 0.0100 0.0008
80 1.0647 nan 0.0100 0.0008
100 1.0340 nan 0.0100 0.0007
120 1.0082 nan 0.0100 0.0005
140 0.9869 nan 0.0100 0.0004
160 0.9691 nan 0.0100 0.0002
180 0.9536 nan 0.0100 0.0000
200 0.9396 nan 0.0100 0.0003
220 0.9276 nan 0.0100 0.0002
240 0.9164 nan 0.0100 0.0002
260 0.9060 nan 0.0100 0.0002
280 0.8973 nan 0.0100 0.0002
300 0.8894 nan 0.0100 0.0002
320 0.8825 nan 0.0100 0.0001
340 0.8754 nan 0.0100 0.0001
360 0.8694 nan 0.0100 0.0002
380 0.8639 nan 0.0100 -0.0000
400 0.8588 nan 0.0100 0.0000
420 0.8541 nan 0.0100 -0.0001
440 0.8494 nan 0.0100 0.0000
460 0.8449 nan 0.0100 0.0001
480 0.8407 nan 0.0100 0.0000
500 0.8370 nan 0.0100 -0.0002
520 0.8333 nan 0.0100 0.0000
540 0.8296 nan 0.0100 -0.0000
560 0.8261 nan 0.0100 0.0000
580 0.8230 nan 0.0100 0.0000
600 0.8201 nan 0.0100 0.0000
620 0.8171 nan 0.0100 0.0000
640 0.8145 nan 0.0100 -0.0000
660 0.8116 nan 0.0100 -0.0000
680 0.8087 nan 0.0100 -0.0000
700 0.8064 nan 0.0100 -0.0000
720 0.8040 nan 0.0100 -0.0001
740 0.8015 nan 0.0100 0.0000
760 0.7992 nan 0.0100 -0.0000
780 0.7970 nan 0.0100 -0.0000
800 0.7949 nan 0.0100 -0.0000
820 0.7927 nan 0.0100 0.0000
840 0.7908 nan 0.0100 -0.0000
860 0.7891 nan 0.0100 -0.0001
880 0.7872 nan 0.0100 -0.0001
900 0.7852 nan 0.0100 -0.0001
920 0.7835 nan 0.0100 -0.0000
940 0.7816 nan 0.0100 -0.0000
960 0.7800 nan 0.0100 -0.0001
980 0.7785 nan 0.0100 -0.0001
1000 0.7767 nan 0.0100 -0.0000
1020 0.7751 nan 0.0100 0.0000
1040 0.7735 nan 0.0100 -0.0000
1060 0.7720 nan 0.0100 -0.0000
1080 0.7706 nan 0.0100 -0.0000
1100 0.7692 nan 0.0100 -0.0001
- Fold04.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3241 nan 0.0100 0.0036
2 1.3162 nan 0.0100 0.0039
3 1.3090 nan 0.0100 0.0036
4 1.3017 nan 0.0100 0.0035
5 1.2945 nan 0.0100 0.0037
6 1.2878 nan 0.0100 0.0035
7 1.2806 nan 0.0100 0.0034
8 1.2739 nan 0.0100 0.0034
9 1.2671 nan 0.0100 0.0033
10 1.2608 nan 0.0100 0.0031
20 1.2025 nan 0.0100 0.0027
40 1.1107 nan 0.0100 0.0018
60 1.0444 nan 0.0100 0.0014
80 0.9946 nan 0.0100 0.0010
100 0.9564 nan 0.0100 0.0006
120 0.9263 nan 0.0100 0.0003
140 0.9029 nan 0.0100 0.0005
160 0.8833 nan 0.0100 0.0002
180 0.8671 nan 0.0100 0.0002
200 0.8532 nan 0.0100 0.0001
220 0.8426 nan 0.0100 0.0001
240 0.8334 nan 0.0100 0.0001
260 0.8245 nan 0.0100 0.0001
280 0.8166 nan 0.0100 0.0001
300 0.8092 nan 0.0100 0.0000
320 0.8023 nan 0.0100 0.0001
340 0.7959 nan 0.0100 0.0001
360 0.7896 nan 0.0100 0.0000
380 0.7838 nan 0.0100 0.0001
400 0.7790 nan 0.0100 -0.0001
420 0.7738 nan 0.0100 0.0000
440 0.7694 nan 0.0100 0.0000
460 0.7650 nan 0.0100 -0.0000
480 0.7612 nan 0.0100 -0.0001
500 0.7572 nan 0.0100 -0.0002
520 0.7533 nan 0.0100 -0.0000
540 0.7497 nan 0.0100 -0.0000
560 0.7462 nan 0.0100 -0.0001
580 0.7431 nan 0.0100 -0.0001
600 0.7401 nan 0.0100 -0.0000
620 0.7376 nan 0.0100 -0.0001
640 0.7345 nan 0.0100 0.0000
660 0.7321 nan 0.0100 -0.0000
680 0.7294 nan 0.0100 -0.0002
700 0.7268 nan 0.0100 -0.0001
720 0.7241 nan 0.0100 -0.0001
740 0.7212 nan 0.0100 -0.0001
760 0.7188 nan 0.0100 -0.0001
780 0.7164 nan 0.0100 -0.0001
800 0.7138 nan 0.0100 -0.0000
820 0.7118 nan 0.0100 -0.0001
840 0.7096 nan 0.0100 -0.0001
860 0.7071 nan 0.0100 -0.0001
880 0.7049 nan 0.0100 -0.0000
900 0.7028 nan 0.0100 -0.0001
920 0.7007 nan 0.0100 -0.0001
940 0.6983 nan 0.0100 -0.0002
960 0.6965 nan 0.0100 -0.0000
980 0.6943 nan 0.0100 -0.0001
1000 0.6922 nan 0.0100 -0.0000
1020 0.6897 nan 0.0100 -0.0001
1040 0.6880 nan 0.0100 -0.0000
1060 0.6862 nan 0.0100 -0.0001
1080 0.6842 nan 0.0100 -0.0001
1100 0.6825 nan 0.0100 -0.0001
- Fold04.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3236 nan 0.0100 0.0038
2 1.3158 nan 0.0100 0.0036
3 1.3074 nan 0.0100 0.0039
4 1.2996 nan 0.0100 0.0040
5 1.2918 nan 0.0100 0.0037
6 1.2843 nan 0.0100 0.0037
7 1.2766 nan 0.0100 0.0037
8 1.2692 nan 0.0100 0.0034
9 1.2622 nan 0.0100 0.0036
10 1.2551 nan 0.0100 0.0035
20 1.1898 nan 0.0100 0.0026
40 1.0905 nan 0.0100 0.0021
60 1.0166 nan 0.0100 0.0015
80 0.9620 nan 0.0100 0.0011
100 0.9213 nan 0.0100 0.0007
120 0.8923 nan 0.0100 0.0006
140 0.8670 nan 0.0100 0.0004
160 0.8471 nan 0.0100 0.0002
180 0.8311 nan 0.0100 0.0001
200 0.8173 nan 0.0100 0.0001
220 0.8050 nan 0.0100 0.0000
240 0.7949 nan 0.0100 -0.0000
260 0.7851 nan 0.0100 0.0001
280 0.7763 nan 0.0100 0.0001
300 0.7678 nan 0.0100 0.0002
320 0.7602 nan 0.0100 0.0002
340 0.7537 nan 0.0100 -0.0001
360 0.7471 nan 0.0100 0.0000
380 0.7411 nan 0.0100 -0.0001
400 0.7360 nan 0.0100 -0.0000
420 0.7311 nan 0.0100 -0.0000
440 0.7256 nan 0.0100 -0.0001
460 0.7209 nan 0.0100 -0.0001
480 0.7161 nan 0.0100 -0.0001
500 0.7116 nan 0.0100 -0.0001
520 0.7071 nan 0.0100 -0.0001
540 0.7030 nan 0.0100 -0.0000
560 0.6987 nan 0.0100 -0.0000
580 0.6946 nan 0.0100 -0.0001
600 0.6910 nan 0.0100 -0.0000
620 0.6867 nan 0.0100 -0.0000
640 0.6829 nan 0.0100 -0.0002
660 0.6793 nan 0.0100 -0.0000
680 0.6763 nan 0.0100 -0.0001
700 0.6732 nan 0.0100 -0.0001
720 0.6700 nan 0.0100 -0.0001
740 0.6671 nan 0.0100 -0.0001
760 0.6641 nan 0.0100 -0.0001
780 0.6611 nan 0.0100 -0.0001
800 0.6581 nan 0.0100 -0.0001
820 0.6552 nan 0.0100 -0.0001
840 0.6525 nan 0.0100 -0.0002
860 0.6498 nan 0.0100 -0.0001
880 0.6471 nan 0.0100 -0.0002
900 0.6444 nan 0.0100 -0.0002
920 0.6416 nan 0.0100 -0.0001
940 0.6386 nan 0.0100 -0.0001
960 0.6358 nan 0.0100 -0.0000
980 0.6331 nan 0.0100 -0.0001
1000 0.6306 nan 0.0100 -0.0002
1020 0.6277 nan 0.0100 -0.0001
1040 0.6248 nan 0.0100 -0.0001
1060 0.6228 nan 0.0100 -0.0002
1080 0.6202 nan 0.0100 -0.0002
1100 0.6178 nan 0.0100 -0.0001
- Fold04.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2744 nan 0.1000 0.0299
2 1.2218 nan 0.1000 0.0240
3 1.1791 nan 0.1000 0.0202
4 1.1433 nan 0.1000 0.0165
5 1.1190 nan 0.1000 0.0133
6 1.1009 nan 0.1000 0.0082
7 1.0791 nan 0.1000 0.0109
8 1.0593 nan 0.1000 0.0092
9 1.0424 nan 0.1000 0.0072
10 1.0282 nan 0.1000 0.0064
20 0.9353 nan 0.1000 0.0024
40 0.8548 nan 0.1000 0.0003
60 0.8185 nan 0.1000 0.0003
80 0.7954 nan 0.1000 -0.0006
100 0.7775 nan 0.1000 -0.0003
120 0.7681 nan 0.1000 -0.0007
140 0.7563 nan 0.1000 -0.0003
160 0.7469 nan 0.1000 -0.0003
180 0.7390 nan 0.1000 -0.0007
200 0.7338 nan 0.1000 -0.0008
220 0.7268 nan 0.1000 -0.0011
240 0.7201 nan 0.1000 -0.0012
260 0.7152 nan 0.1000 -0.0010
280 0.7137 nan 0.1000 -0.0006
300 0.7098 nan 0.1000 -0.0010
320 0.7050 nan 0.1000 -0.0003
340 0.7005 nan 0.1000 -0.0011
360 0.6968 nan 0.1000 -0.0011
380 0.6928 nan 0.1000 -0.0013
400 0.6900 nan 0.1000 -0.0008
420 0.6858 nan 0.1000 -0.0004
440 0.6831 nan 0.1000 -0.0009
460 0.6792 nan 0.1000 -0.0004
480 0.6760 nan 0.1000 -0.0014
500 0.6740 nan 0.1000 -0.0006
520 0.6715 nan 0.1000 -0.0009
540 0.6694 nan 0.1000 -0.0014
560 0.6679 nan 0.1000 -0.0008
580 0.6663 nan 0.1000 -0.0010
600 0.6640 nan 0.1000 -0.0007
620 0.6611 nan 0.1000 -0.0004
640 0.6594 nan 0.1000 -0.0005
660 0.6562 nan 0.1000 -0.0011
680 0.6543 nan 0.1000 -0.0008
700 0.6523 nan 0.1000 -0.0006
720 0.6519 nan 0.1000 -0.0011
740 0.6502 nan 0.1000 -0.0008
760 0.6466 nan 0.1000 -0.0005
780 0.6441 nan 0.1000 -0.0003
800 0.6432 nan 0.1000 -0.0007
820 0.6412 nan 0.1000 -0.0016
840 0.6400 nan 0.1000 -0.0003
860 0.6379 nan 0.1000 -0.0004
880 0.6358 nan 0.1000 -0.0013
900 0.6334 nan 0.1000 -0.0005
920 0.6321 nan 0.1000 -0.0007
940 0.6303 nan 0.1000 -0.0009
960 0.6282 nan 0.1000 -0.0003
980 0.6265 nan 0.1000 -0.0006
1000 0.6257 nan 0.1000 -0.0005
1020 0.6235 nan 0.1000 -0.0004
1040 0.6216 nan 0.1000 -0.0009
1060 0.6194 nan 0.1000 -0.0019
1080 0.6183 nan 0.1000 -0.0009
1100 0.6165 nan 0.1000 -0.0003
- Fold04.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2643 nan 0.1000 0.0365
2 1.2028 nan 0.1000 0.0306
3 1.1541 nan 0.1000 0.0253
4 1.1120 nan 0.1000 0.0219
5 1.0730 nan 0.1000 0.0172
6 1.0413 nan 0.1000 0.0153
7 1.0146 nan 0.1000 0.0121
8 0.9915 nan 0.1000 0.0111
9 0.9718 nan 0.1000 0.0099
10 0.9546 nan 0.1000 0.0081
20 0.8513 nan 0.1000 0.0032
40 0.7799 nan 0.1000 -0.0012
60 0.7403 nan 0.1000 -0.0015
80 0.7151 nan 0.1000 -0.0014
100 0.6940 nan 0.1000 -0.0012
120 0.6765 nan 0.1000 -0.0014
140 0.6584 nan 0.1000 -0.0020
160 0.6418 nan 0.1000 -0.0003
180 0.6298 nan 0.1000 -0.0009
200 0.6178 nan 0.1000 -0.0017
220 0.6034 nan 0.1000 -0.0010
240 0.5911 nan 0.1000 -0.0006
260 0.5807 nan 0.1000 -0.0004
280 0.5713 nan 0.1000 -0.0016
300 0.5602 nan 0.1000 -0.0011
320 0.5516 nan 0.1000 -0.0005
340 0.5439 nan 0.1000 -0.0005
360 0.5369 nan 0.1000 -0.0013
380 0.5306 nan 0.1000 -0.0010
400 0.5234 nan 0.1000 -0.0015
420 0.5180 nan 0.1000 -0.0010
440 0.5110 nan 0.1000 -0.0001
460 0.5039 nan 0.1000 -0.0009
480 0.4963 nan 0.1000 -0.0002
500 0.4898 nan 0.1000 -0.0007
520 0.4856 nan 0.1000 -0.0010
540 0.4775 nan 0.1000 -0.0007
560 0.4724 nan 0.1000 -0.0012
580 0.4652 nan 0.1000 -0.0002
600 0.4602 nan 0.1000 -0.0008
620 0.4560 nan 0.1000 -0.0021
640 0.4494 nan 0.1000 -0.0012
660 0.4462 nan 0.1000 -0.0011
680 0.4393 nan 0.1000 -0.0012
700 0.4355 nan 0.1000 -0.0004
720 0.4309 nan 0.1000 -0.0007
740 0.4271 nan 0.1000 -0.0011
760 0.4231 nan 0.1000 -0.0007
780 0.4189 nan 0.1000 -0.0008
800 0.4132 nan 0.1000 -0.0003
820 0.4102 nan 0.1000 -0.0011
840 0.4069 nan 0.1000 -0.0011
860 0.4030 nan 0.1000 -0.0006
880 0.3986 nan 0.1000 -0.0006
900 0.3953 nan 0.1000 -0.0004
920 0.3914 nan 0.1000 -0.0006
940 0.3874 nan 0.1000 -0.0007
960 0.3838 nan 0.1000 -0.0008
980 0.3800 nan 0.1000 -0.0010
1000 0.3789 nan 0.1000 -0.0010
1020 0.3761 nan 0.1000 -0.0010
1040 0.3716 nan 0.1000 -0.0008
1060 0.3691 nan 0.1000 -0.0012
1080 0.3672 nan 0.1000 -0.0007
1100 0.3637 nan 0.1000 -0.0011
- Fold04.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold04.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2460 nan 0.1000 0.0371
2 1.1810 nan 0.1000 0.0320
3 1.1319 nan 0.1000 0.0241
4 1.0854 nan 0.1000 0.0185
5 1.0471 nan 0.1000 0.0190
6 1.0159 nan 0.1000 0.0166
7 0.9862 nan 0.1000 0.0139
8 0.9574 nan 0.1000 0.0117
9 0.9315 nan 0.1000 0.0096
10 0.9132 nan 0.1000 0.0070
20 0.8193 nan 0.1000 0.0011
40 0.7407 nan 0.1000 0.0007
60 0.6964 nan 0.1000 -0.0007
80 0.6584 nan 0.1000 -0.0010
100 0.6338 nan 0.1000 -0.0010
120 0.6110 nan 0.1000 -0.0015
140 0.5897 nan 0.1000 -0.0010
160 0.5708 nan 0.1000 -0.0008
180 0.5592 nan 0.1000 -0.0012
200 0.5467 nan 0.1000 -0.0009
220 0.5324 nan 0.1000 -0.0018
240 0.5172 nan 0.1000 -0.0008
260 0.5004 nan 0.1000 -0.0007
280 0.4852 nan 0.1000 -0.0013
300 0.4736 nan 0.1000 -0.0009
320 0.4619 nan 0.1000 -0.0017
340 0.4530 nan 0.1000 -0.0013
360 0.4418 nan 0.1000 -0.0012
380 0.4338 nan 0.1000 -0.0006
400 0.4252 nan 0.1000 -0.0008
420 0.4146 nan 0.1000 -0.0007
440 0.4054 nan 0.1000 -0.0007
460 0.3942 nan 0.1000 -0.0013
480 0.3860 nan 0.1000 -0.0011
500 0.3776 nan 0.1000 -0.0009
520 0.3696 nan 0.1000 -0.0009
540 0.3644 nan 0.1000 -0.0004
560 0.3579 nan 0.1000 -0.0015
580 0.3505 nan 0.1000 -0.0017
600 0.3441 nan 0.1000 -0.0011
620 0.3397 nan 0.1000 -0.0008
640 0.3326 nan 0.1000 -0.0010
660 0.3265 nan 0.1000 -0.0007
680 0.3220 nan 0.1000 -0.0009
700 0.3169 nan 0.1000 -0.0008
720 0.3114 nan 0.1000 -0.0009
740 0.3061 nan 0.1000 -0.0009
760 0.3023 nan 0.1000 -0.0008
780 0.2973 nan 0.1000 -0.0010
800 0.2919 nan 0.1000 -0.0003
820 0.2874 nan 0.1000 -0.0008
840 0.2819 nan 0.1000 -0.0014
860 0.2761 nan 0.1000 -0.0004
880 0.2708 nan 0.1000 -0.0007
900 0.2681 nan 0.1000 -0.0008
920 0.2629 nan 0.1000 -0.0006
940 0.2603 nan 0.1000 -0.0014
960 0.2569 nan 0.1000 -0.0013
980 0.2528 nan 0.1000 -0.0014
1000 0.2491 nan 0.1000 -0.0012
1020 0.2465 nan 0.1000 -0.0008
1040 0.2437 nan 0.1000 -0.0006
1060 0.2397 nan 0.1000 -0.0004
1080 0.2369 nan 0.1000 -0.0008
1100 0.2329 nan 0.1000 -0.0004
- Fold04.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3253 nan 0.0100 0.0031
2 1.3198 nan 0.0100 0.0029
3 1.3140 nan 0.0100 0.0030
4 1.3081 nan 0.0100 0.0027
5 1.3029 nan 0.0100 0.0028
6 1.2974 nan 0.0100 0.0028
7 1.2915 nan 0.0100 0.0027
8 1.2864 nan 0.0100 0.0027
9 1.2812 nan 0.0100 0.0026
10 1.2761 nan 0.0100 0.0026
20 1.2285 nan 0.0100 0.0021
40 1.1566 nan 0.0100 0.0015
60 1.1087 nan 0.0100 0.0010
80 1.0710 nan 0.0100 0.0007
100 1.0396 nan 0.0100 0.0006
120 1.0140 nan 0.0100 0.0005
140 0.9921 nan 0.0100 0.0004
160 0.9726 nan 0.0100 0.0003
180 0.9571 nan 0.0100 0.0002
200 0.9433 nan 0.0100 0.0002
220 0.9314 nan 0.0100 0.0001
240 0.9204 nan 0.0100 0.0002
260 0.9104 nan 0.0100 -0.0000
280 0.9015 nan 0.0100 0.0002
300 0.8933 nan 0.0100 0.0001
320 0.8861 nan 0.0100 0.0001
340 0.8793 nan 0.0100 0.0001
360 0.8736 nan 0.0100 0.0001
380 0.8677 nan 0.0100 0.0001
400 0.8625 nan 0.0100 -0.0001
420 0.8576 nan 0.0100 -0.0000
440 0.8527 nan 0.0100 -0.0000
460 0.8482 nan 0.0100 0.0001
480 0.8439 nan 0.0100 0.0000
500 0.8398 nan 0.0100 -0.0000
520 0.8362 nan 0.0100 -0.0000
540 0.8325 nan 0.0100 -0.0000
560 0.8291 nan 0.0100 0.0000
580 0.8258 nan 0.0100 0.0000
600 0.8224 nan 0.0100 -0.0000
620 0.8192 nan 0.0100 0.0000
640 0.8165 nan 0.0100 -0.0000
660 0.8137 nan 0.0100 -0.0000
680 0.8105 nan 0.0100 0.0000
700 0.8080 nan 0.0100 0.0000
720 0.8052 nan 0.0100 -0.0000
740 0.8028 nan 0.0100 -0.0000
760 0.8006 nan 0.0100 -0.0000
780 0.7984 nan 0.0100 -0.0000
800 0.7960 nan 0.0100 0.0000
820 0.7938 nan 0.0100 -0.0000
840 0.7916 nan 0.0100 -0.0000
860 0.7897 nan 0.0100 -0.0001
880 0.7874 nan 0.0100 -0.0001
900 0.7857 nan 0.0100 -0.0001
920 0.7836 nan 0.0100 -0.0000
940 0.7816 nan 0.0100 -0.0001
960 0.7799 nan 0.0100 -0.0000
980 0.7782 nan 0.0100 -0.0000
1000 0.7764 nan 0.0100 0.0000
1020 0.7748 nan 0.0100 -0.0000
1040 0.7733 nan 0.0100 -0.0000
1060 0.7718 nan 0.0100 -0.0001
1080 0.7702 nan 0.0100 -0.0000
1100 0.7687 nan 0.0100 -0.0001
- Fold05.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3242 nan 0.0100 0.0040
2 1.3166 nan 0.0100 0.0037
3 1.3091 nan 0.0100 0.0037
4 1.3019 nan 0.0100 0.0035
5 1.2945 nan 0.0100 0.0033
6 1.2876 nan 0.0100 0.0035
7 1.2805 nan 0.0100 0.0035
8 1.2738 nan 0.0100 0.0033
9 1.2672 nan 0.0100 0.0030
10 1.2607 nan 0.0100 0.0030
20 1.2016 nan 0.0100 0.0026
40 1.1132 nan 0.0100 0.0019
60 1.0480 nan 0.0100 0.0014
80 0.9979 nan 0.0100 0.0010
100 0.9608 nan 0.0100 0.0008
120 0.9308 nan 0.0100 0.0005
140 0.9069 nan 0.0100 0.0005
160 0.8876 nan 0.0100 0.0004
180 0.8707 nan 0.0100 0.0004
200 0.8568 nan 0.0100 0.0002
220 0.8456 nan 0.0100 -0.0001
240 0.8344 nan 0.0100 0.0001
260 0.8248 nan 0.0100 0.0000
280 0.8159 nan 0.0100 0.0001
300 0.8077 nan 0.0100 0.0001
320 0.8002 nan 0.0100 0.0000
340 0.7938 nan 0.0100 -0.0000
360 0.7869 nan 0.0100 -0.0000
380 0.7810 nan 0.0100 0.0000
400 0.7759 nan 0.0100 0.0000
420 0.7709 nan 0.0100 -0.0001
440 0.7662 nan 0.0100 -0.0001
460 0.7610 nan 0.0100 -0.0000
480 0.7567 nan 0.0100 -0.0001
500 0.7525 nan 0.0100 -0.0001
520 0.7486 nan 0.0100 0.0000
540 0.7451 nan 0.0100 -0.0000
560 0.7414 nan 0.0100 -0.0000
580 0.7382 nan 0.0100 -0.0000
600 0.7356 nan 0.0100 -0.0002
620 0.7324 nan 0.0100 -0.0001
640 0.7292 nan 0.0100 -0.0001
660 0.7260 nan 0.0100 0.0000
680 0.7233 nan 0.0100 -0.0000
700 0.7209 nan 0.0100 0.0000
720 0.7183 nan 0.0100 -0.0001
740 0.7157 nan 0.0100 -0.0001
760 0.7131 nan 0.0100 -0.0001
780 0.7105 nan 0.0100 -0.0001
800 0.7077 nan 0.0100 0.0000
820 0.7053 nan 0.0100 -0.0001
840 0.7030 nan 0.0100 -0.0001
860 0.7012 nan 0.0100 -0.0000
880 0.6991 nan 0.0100 -0.0001
900 0.6971 nan 0.0100 -0.0000
920 0.6946 nan 0.0100 -0.0001
940 0.6923 nan 0.0100 -0.0000
960 0.6899 nan 0.0100 -0.0001
980 0.6882 nan 0.0100 -0.0001
1000 0.6863 nan 0.0100 -0.0000
1020 0.6842 nan 0.0100 -0.0001
1040 0.6822 nan 0.0100 -0.0002
1060 0.6806 nan 0.0100 -0.0001
1080 0.6789 nan 0.0100 -0.0002
1100 0.6770 nan 0.0100 -0.0001
- Fold05.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3245 nan 0.0100 0.0039
2 1.3167 nan 0.0100 0.0038
3 1.3085 nan 0.0100 0.0041
4 1.3006 nan 0.0100 0.0040
5 1.2932 nan 0.0100 0.0034
6 1.2858 nan 0.0100 0.0035
7 1.2786 nan 0.0100 0.0033
8 1.2714 nan 0.0100 0.0032
9 1.2640 nan 0.0100 0.0035
10 1.2570 nan 0.0100 0.0036
20 1.1941 nan 0.0100 0.0029
40 1.0950 nan 0.0100 0.0021
60 1.0225 nan 0.0100 0.0015
80 0.9683 nan 0.0100 0.0011
100 0.9269 nan 0.0100 0.0007
120 0.8949 nan 0.0100 0.0006
140 0.8689 nan 0.0100 0.0004
160 0.8486 nan 0.0100 0.0001
180 0.8315 nan 0.0100 0.0003
200 0.8171 nan 0.0100 0.0001
220 0.8041 nan 0.0100 0.0001
240 0.7919 nan 0.0100 0.0000
260 0.7825 nan 0.0100 -0.0000
280 0.7733 nan 0.0100 0.0000
300 0.7648 nan 0.0100 0.0001
320 0.7576 nan 0.0100 0.0000
340 0.7512 nan 0.0100 -0.0000
360 0.7439 nan 0.0100 0.0000
380 0.7376 nan 0.0100 0.0000
400 0.7317 nan 0.0100 0.0001
420 0.7261 nan 0.0100 -0.0001
440 0.7214 nan 0.0100 -0.0000
460 0.7157 nan 0.0100 -0.0001
480 0.7104 nan 0.0100 -0.0001
500 0.7055 nan 0.0100 0.0000
520 0.7013 nan 0.0100 0.0000
540 0.6970 nan 0.0100 -0.0001
560 0.6933 nan 0.0100 -0.0001
580 0.6898 nan 0.0100 -0.0002
600 0.6858 nan 0.0100 0.0000
620 0.6814 nan 0.0100 0.0000
640 0.6779 nan 0.0100 -0.0001
660 0.6740 nan 0.0100 -0.0002
680 0.6711 nan 0.0100 -0.0000
700 0.6679 nan 0.0100 -0.0002
720 0.6643 nan 0.0100 -0.0001
740 0.6614 nan 0.0100 -0.0000
760 0.6584 nan 0.0100 -0.0001
780 0.6557 nan 0.0100 -0.0001
800 0.6528 nan 0.0100 -0.0002
820 0.6503 nan 0.0100 -0.0000
840 0.6473 nan 0.0100 -0.0003
860 0.6447 nan 0.0100 -0.0002
880 0.6419 nan 0.0100 -0.0001
900 0.6394 nan 0.0100 -0.0000
920 0.6367 nan 0.0100 -0.0000
940 0.6338 nan 0.0100 -0.0000
960 0.6313 nan 0.0100 -0.0002
980 0.6289 nan 0.0100 -0.0001
1000 0.6267 nan 0.0100 -0.0001
1020 0.6243 nan 0.0100 -0.0001
1040 0.6217 nan 0.0100 -0.0001
1060 0.6193 nan 0.0100 -0.0002
1080 0.6173 nan 0.0100 -0.0001
1100 0.6150 nan 0.0100 -0.0000
- Fold05.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2688 nan 0.1000 0.0293
2 1.2216 nan 0.1000 0.0234
3 1.1859 nan 0.1000 0.0189
4 1.1507 nan 0.1000 0.0160
5 1.1210 nan 0.1000 0.0131
6 1.1015 nan 0.1000 0.0072
7 1.0833 nan 0.1000 0.0073
8 1.0622 nan 0.1000 0.0095
9 1.0421 nan 0.1000 0.0082
10 1.0265 nan 0.1000 0.0067
20 0.9379 nan 0.1000 0.0010
40 0.8618 nan 0.1000 0.0010
60 0.8264 nan 0.1000 -0.0011
80 0.8021 nan 0.1000 -0.0003
100 0.7819 nan 0.1000 -0.0003
120 0.7667 nan 0.1000 -0.0004
140 0.7546 nan 0.1000 -0.0012
160 0.7462 nan 0.1000 -0.0012
180 0.7396 nan 0.1000 -0.0005
200 0.7333 nan 0.1000 -0.0003
220 0.7286 nan 0.1000 -0.0001
240 0.7239 nan 0.1000 -0.0009
260 0.7186 nan 0.1000 -0.0000
280 0.7154 nan 0.1000 -0.0008
300 0.7113 nan 0.1000 -0.0007
320 0.7069 nan 0.1000 -0.0007
340 0.7044 nan 0.1000 -0.0007
360 0.7014 nan 0.1000 -0.0004
380 0.6971 nan 0.1000 -0.0008
400 0.6952 nan 0.1000 -0.0009
420 0.6919 nan 0.1000 -0.0015
440 0.6876 nan 0.1000 -0.0005
460 0.6845 nan 0.1000 -0.0007
480 0.6814 nan 0.1000 -0.0008
500 0.6800 nan 0.1000 -0.0010
520 0.6775 nan 0.1000 -0.0004
540 0.6761 nan 0.1000 -0.0003
560 0.6742 nan 0.1000 -0.0010
580 0.6723 nan 0.1000 -0.0010
600 0.6707 nan 0.1000 -0.0006
620 0.6685 nan 0.1000 -0.0006
640 0.6663 nan 0.1000 -0.0013
660 0.6644 nan 0.1000 -0.0009
680 0.6611 nan 0.1000 -0.0005
700 0.6587 nan 0.1000 -0.0004
720 0.6580 nan 0.1000 -0.0009
740 0.6562 nan 0.1000 -0.0010
760 0.6541 nan 0.1000 -0.0006
780 0.6524 nan 0.1000 -0.0010
800 0.6520 nan 0.1000 -0.0006
820 0.6513 nan 0.1000 -0.0007
840 0.6501 nan 0.1000 -0.0006
860 0.6483 nan 0.1000 -0.0004
880 0.6471 nan 0.1000 -0.0009
900 0.6448 nan 0.1000 -0.0007
920 0.6434 nan 0.1000 -0.0005
940 0.6433 nan 0.1000 -0.0010
960 0.6405 nan 0.1000 -0.0007
980 0.6396 nan 0.1000 -0.0010
1000 0.6396 nan 0.1000 -0.0006
1020 0.6361 nan 0.1000 -0.0008
1040 0.6340 nan 0.1000 -0.0006
1060 0.6339 nan 0.1000 -0.0007
1080 0.6332 nan 0.1000 -0.0006
1100 0.6325 nan 0.1000 -0.0009
- Fold05.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2592 nan 0.1000 0.0363
2 1.2003 nan 0.1000 0.0301
3 1.1497 nan 0.1000 0.0247
4 1.1088 nan 0.1000 0.0209
5 1.0732 nan 0.1000 0.0163
6 1.0435 nan 0.1000 0.0141
7 1.0171 nan 0.1000 0.0130
8 0.9948 nan 0.1000 0.0100
9 0.9737 nan 0.1000 0.0094
10 0.9561 nan 0.1000 0.0085
20 0.8538 nan 0.1000 0.0026
40 0.7744 nan 0.1000 -0.0000
60 0.7347 nan 0.1000 -0.0010
80 0.7078 nan 0.1000 -0.0006
100 0.6851 nan 0.1000 -0.0008
120 0.6701 nan 0.1000 -0.0006
140 0.6530 nan 0.1000 -0.0010
160 0.6356 nan 0.1000 -0.0011
180 0.6224 nan 0.1000 0.0004
200 0.6106 nan 0.1000 -0.0008
220 0.6029 nan 0.1000 -0.0006
240 0.5925 nan 0.1000 -0.0012
260 0.5859 nan 0.1000 -0.0009
280 0.5765 nan 0.1000 -0.0014
300 0.5645 nan 0.1000 -0.0003
320 0.5565 nan 0.1000 -0.0009
340 0.5505 nan 0.1000 -0.0014
360 0.5404 nan 0.1000 -0.0013
380 0.5345 nan 0.1000 -0.0016
400 0.5252 nan 0.1000 -0.0010
420 0.5205 nan 0.1000 -0.0008
440 0.5145 nan 0.1000 -0.0009
460 0.5099 nan 0.1000 -0.0009
480 0.5066 nan 0.1000 -0.0018
500 0.4995 nan 0.1000 -0.0010
520 0.4947 nan 0.1000 -0.0009
540 0.4886 nan 0.1000 -0.0006
560 0.4817 nan 0.1000 -0.0011
580 0.4760 nan 0.1000 -0.0008
600 0.4715 nan 0.1000 -0.0008
620 0.4644 nan 0.1000 -0.0007
640 0.4591 nan 0.1000 -0.0006
660 0.4529 nan 0.1000 -0.0011
680 0.4491 nan 0.1000 -0.0007
700 0.4432 nan 0.1000 -0.0008
720 0.4385 nan 0.1000 -0.0010
740 0.4346 nan 0.1000 -0.0006
760 0.4311 nan 0.1000 -0.0007
780 0.4285 nan 0.1000 -0.0014
800 0.4236 nan 0.1000 -0.0006
820 0.4201 nan 0.1000 -0.0010
840 0.4161 nan 0.1000 -0.0010
860 0.4133 nan 0.1000 -0.0008
880 0.4115 nan 0.1000 -0.0006
900 0.4070 nan 0.1000 -0.0013
920 0.4027 nan 0.1000 -0.0007
940 0.3995 nan 0.1000 -0.0008
960 0.3942 nan 0.1000 -0.0005
980 0.3917 nan 0.1000 -0.0011
1000 0.3898 nan 0.1000 -0.0008
1020 0.3865 nan 0.1000 -0.0006
1040 0.3831 nan 0.1000 -0.0001
1060 0.3803 nan 0.1000 -0.0006
1080 0.3763 nan 0.1000 -0.0002
1100 0.3732 nan 0.1000 -0.0012
- Fold05.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold05.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2526 nan 0.1000 0.0366
2 1.1844 nan 0.1000 0.0328
3 1.1323 nan 0.1000 0.0250
4 1.0845 nan 0.1000 0.0212
5 1.0468 nan 0.1000 0.0165
6 1.0158 nan 0.1000 0.0163
7 0.9872 nan 0.1000 0.0137
8 0.9602 nan 0.1000 0.0127
9 0.9376 nan 0.1000 0.0088
10 0.9198 nan 0.1000 0.0080
20 0.8192 nan 0.1000 0.0024
40 0.7326 nan 0.1000 -0.0006
60 0.6884 nan 0.1000 -0.0014
80 0.6607 nan 0.1000 -0.0009
100 0.6385 nan 0.1000 -0.0015
120 0.6177 nan 0.1000 -0.0018
140 0.5945 nan 0.1000 -0.0016
160 0.5760 nan 0.1000 -0.0011
180 0.5570 nan 0.1000 -0.0009
200 0.5442 nan 0.1000 -0.0014
220 0.5301 nan 0.1000 -0.0011
240 0.5140 nan 0.1000 -0.0020
260 0.5005 nan 0.1000 -0.0014
280 0.4906 nan 0.1000 -0.0016
300 0.4808 nan 0.1000 -0.0013
320 0.4684 nan 0.1000 -0.0010
340 0.4593 nan 0.1000 -0.0003
360 0.4502 nan 0.1000 -0.0009
380 0.4402 nan 0.1000 -0.0010
400 0.4337 nan 0.1000 -0.0017
420 0.4258 nan 0.1000 -0.0014
440 0.4158 nan 0.1000 -0.0006
460 0.4053 nan 0.1000 -0.0006
480 0.3975 nan 0.1000 -0.0007
500 0.3894 nan 0.1000 -0.0013
520 0.3801 nan 0.1000 -0.0013
540 0.3730 nan 0.1000 -0.0019
560 0.3661 nan 0.1000 -0.0013
580 0.3606 nan 0.1000 -0.0009
600 0.3538 nan 0.1000 -0.0009
620 0.3483 nan 0.1000 -0.0011
640 0.3427 nan 0.1000 -0.0007
660 0.3380 nan 0.1000 -0.0010
680 0.3325 nan 0.1000 -0.0005
700 0.3273 nan 0.1000 -0.0015
720 0.3208 nan 0.1000 -0.0009
740 0.3148 nan 0.1000 -0.0016
760 0.3114 nan 0.1000 -0.0011
780 0.3071 nan 0.1000 -0.0014
800 0.3017 nan 0.1000 -0.0010
820 0.2991 nan 0.1000 -0.0012
840 0.2956 nan 0.1000 -0.0007
860 0.2917 nan 0.1000 -0.0005
880 0.2880 nan 0.1000 -0.0007
900 0.2828 nan 0.1000 -0.0008
920 0.2792 nan 0.1000 -0.0007
940 0.2747 nan 0.1000 -0.0012
960 0.2707 nan 0.1000 -0.0009
980 0.2685 nan 0.1000 -0.0008
1000 0.2652 nan 0.1000 -0.0010
1020 0.2621 nan 0.1000 -0.0005
1040 0.2580 nan 0.1000 -0.0009
1060 0.2554 nan 0.1000 -0.0005
1080 0.2510 nan 0.1000 -0.0007
1100 0.2488 nan 0.1000 -0.0005
- Fold05.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3254 nan 0.0100 0.0031
2 1.3191 nan 0.0100 0.0029
3 1.3132 nan 0.0100 0.0029
4 1.3079 nan 0.0100 0.0030
5 1.3015 nan 0.0100 0.0029
6 1.2954 nan 0.0100 0.0028
7 1.2897 nan 0.0100 0.0028
8 1.2844 nan 0.0100 0.0027
9 1.2786 nan 0.0100 0.0026
10 1.2731 nan 0.0100 0.0026
20 1.2244 nan 0.0100 0.0021
40 1.1528 nan 0.0100 0.0015
60 1.1017 nan 0.0100 0.0011
80 1.0647 nan 0.0100 0.0007
100 1.0339 nan 0.0100 0.0006
120 1.0079 nan 0.0100 0.0005
140 0.9873 nan 0.0100 0.0004
160 0.9692 nan 0.0100 0.0003
180 0.9545 nan 0.0100 0.0002
200 0.9410 nan 0.0100 0.0002
220 0.9292 nan 0.0100 0.0002
240 0.9185 nan 0.0100 0.0002
260 0.9086 nan 0.0100 0.0002
280 0.8997 nan 0.0100 0.0001
300 0.8919 nan 0.0100 0.0001
320 0.8848 nan 0.0100 0.0001
340 0.8774 nan 0.0100 0.0001
360 0.8713 nan 0.0100 0.0001
380 0.8662 nan 0.0100 -0.0000
400 0.8610 nan 0.0100 0.0000
420 0.8558 nan 0.0100 0.0000
440 0.8511 nan 0.0100 0.0000
460 0.8465 nan 0.0100 0.0001
480 0.8421 nan 0.0100 0.0001
500 0.8383 nan 0.0100 -0.0000
520 0.8347 nan 0.0100 -0.0000
540 0.8310 nan 0.0100 0.0000
560 0.8277 nan 0.0100 -0.0000
580 0.8244 nan 0.0100 0.0000
600 0.8214 nan 0.0100 0.0000
620 0.8185 nan 0.0100 0.0000
640 0.8158 nan 0.0100 0.0000
660 0.8135 nan 0.0100 0.0000
680 0.8115 nan 0.0100 -0.0000
700 0.8089 nan 0.0100 0.0000
720 0.8066 nan 0.0100 -0.0001
740 0.8042 nan 0.0100 -0.0001
760 0.8020 nan 0.0100 -0.0001
780 0.8000 nan 0.0100 -0.0000
800 0.7980 nan 0.0100 -0.0001
820 0.7961 nan 0.0100 -0.0001
840 0.7942 nan 0.0100 -0.0000
860 0.7927 nan 0.0100 -0.0001
880 0.7910 nan 0.0100 -0.0000
900 0.7894 nan 0.0100 -0.0000
920 0.7876 nan 0.0100 -0.0000
940 0.7860 nan 0.0100 -0.0001
960 0.7845 nan 0.0100 -0.0001
980 0.7829 nan 0.0100 -0.0000
1000 0.7813 nan 0.0100 -0.0001
1020 0.7797 nan 0.0100 -0.0000
1040 0.7781 nan 0.0100 -0.0000
1060 0.7768 nan 0.0100 0.0000
1080 0.7754 nan 0.0100 -0.0000
1100 0.7740 nan 0.0100 -0.0000
- Fold06.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3228 nan 0.0100 0.0037
2 1.3150 nan 0.0100 0.0033
3 1.3073 nan 0.0100 0.0037
4 1.3006 nan 0.0100 0.0032
5 1.2931 nan 0.0100 0.0037
6 1.2858 nan 0.0100 0.0036
7 1.2785 nan 0.0100 0.0032
8 1.2717 nan 0.0100 0.0033
9 1.2650 nan 0.0100 0.0032
10 1.2585 nan 0.0100 0.0033
20 1.1997 nan 0.0100 0.0026
40 1.1073 nan 0.0100 0.0019
60 1.0404 nan 0.0100 0.0012
80 0.9899 nan 0.0100 0.0010
100 0.9530 nan 0.0100 0.0007
120 0.9229 nan 0.0100 0.0005
140 0.9000 nan 0.0100 0.0005
160 0.8806 nan 0.0100 0.0002
180 0.8652 nan 0.0100 0.0002
200 0.8504 nan 0.0100 0.0002
220 0.8394 nan 0.0100 0.0002
240 0.8296 nan 0.0100 0.0000
260 0.8210 nan 0.0100 0.0001
280 0.8130 nan 0.0100 0.0001
300 0.8058 nan 0.0100 -0.0000
320 0.7997 nan 0.0100 0.0000
340 0.7929 nan 0.0100 0.0000
360 0.7868 nan 0.0100 0.0000
380 0.7817 nan 0.0100 0.0000
400 0.7770 nan 0.0100 0.0001
420 0.7730 nan 0.0100 0.0000
440 0.7684 nan 0.0100 0.0000
460 0.7646 nan 0.0100 -0.0000
480 0.7608 nan 0.0100 -0.0001
500 0.7572 nan 0.0100 0.0000
520 0.7540 nan 0.0100 -0.0001
540 0.7508 nan 0.0100 -0.0000
560 0.7474 nan 0.0100 -0.0000
580 0.7440 nan 0.0100 -0.0000
600 0.7413 nan 0.0100 -0.0001
620 0.7379 nan 0.0100 -0.0001
640 0.7351 nan 0.0100 -0.0001
660 0.7325 nan 0.0100 -0.0001
680 0.7297 nan 0.0100 -0.0001
700 0.7267 nan 0.0100 -0.0000
720 0.7243 nan 0.0100 -0.0001
740 0.7217 nan 0.0100 -0.0001
760 0.7194 nan 0.0100 -0.0001
780 0.7171 nan 0.0100 -0.0001
800 0.7147 nan 0.0100 -0.0002
820 0.7122 nan 0.0100 -0.0001
840 0.7098 nan 0.0100 -0.0001
860 0.7075 nan 0.0100 -0.0001
880 0.7055 nan 0.0100 -0.0001
900 0.7034 nan 0.0100 -0.0001
920 0.7014 nan 0.0100 -0.0001
940 0.6991 nan 0.0100 0.0000
960 0.6971 nan 0.0100 0.0000
980 0.6951 nan 0.0100 -0.0001
1000 0.6932 nan 0.0100 -0.0000
1020 0.6914 nan 0.0100 -0.0001
1040 0.6894 nan 0.0100 -0.0002
1060 0.6880 nan 0.0100 -0.0000
1080 0.6860 nan 0.0100 -0.0002
1100 0.6839 nan 0.0100 -0.0001
- Fold06.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3225 nan 0.0100 0.0041
2 1.3147 nan 0.0100 0.0040
3 1.3061 nan 0.0100 0.0040
4 1.2982 nan 0.0100 0.0038
5 1.2901 nan 0.0100 0.0036
6 1.2820 nan 0.0100 0.0037
7 1.2745 nan 0.0100 0.0036
8 1.2670 nan 0.0100 0.0032
9 1.2599 nan 0.0100 0.0035
10 1.2523 nan 0.0100 0.0036
20 1.1872 nan 0.0100 0.0030
40 1.0889 nan 0.0100 0.0018
60 1.0165 nan 0.0100 0.0015
80 0.9618 nan 0.0100 0.0011
100 0.9211 nan 0.0100 0.0006
120 0.8897 nan 0.0100 0.0005
140 0.8657 nan 0.0100 0.0004
160 0.8456 nan 0.0100 0.0004
180 0.8290 nan 0.0100 0.0002
200 0.8151 nan 0.0100 0.0002
220 0.8026 nan 0.0100 0.0001
240 0.7922 nan 0.0100 0.0001
260 0.7826 nan 0.0100 0.0002
280 0.7745 nan 0.0100 0.0001
300 0.7670 nan 0.0100 0.0000
320 0.7603 nan 0.0100 -0.0000
340 0.7537 nan 0.0100 -0.0000
360 0.7478 nan 0.0100 -0.0001
380 0.7413 nan 0.0100 -0.0002
400 0.7355 nan 0.0100 -0.0000
420 0.7303 nan 0.0100 -0.0000
440 0.7257 nan 0.0100 -0.0000
460 0.7209 nan 0.0100 -0.0000
480 0.7164 nan 0.0100 -0.0001
500 0.7125 nan 0.0100 -0.0001
520 0.7082 nan 0.0100 -0.0001
540 0.7043 nan 0.0100 -0.0002
560 0.7002 nan 0.0100 -0.0000
580 0.6971 nan 0.0100 -0.0001
600 0.6933 nan 0.0100 -0.0001
620 0.6899 nan 0.0100 -0.0001
640 0.6861 nan 0.0100 -0.0001
660 0.6830 nan 0.0100 -0.0000
680 0.6790 nan 0.0100 -0.0001
700 0.6755 nan 0.0100 -0.0001
720 0.6725 nan 0.0100 -0.0001
740 0.6696 nan 0.0100 -0.0001
760 0.6670 nan 0.0100 -0.0001
780 0.6637 nan 0.0100 -0.0001
800 0.6608 nan 0.0100 -0.0001
820 0.6573 nan 0.0100 -0.0000
840 0.6547 nan 0.0100 -0.0001
860 0.6522 nan 0.0100 -0.0001
880 0.6488 nan 0.0100 -0.0001
900 0.6466 nan 0.0100 -0.0002
920 0.6436 nan 0.0100 -0.0001
940 0.6407 nan 0.0100 -0.0002
960 0.6384 nan 0.0100 -0.0001
980 0.6358 nan 0.0100 -0.0001
1000 0.6332 nan 0.0100 -0.0001
1020 0.6310 nan 0.0100 -0.0001
1040 0.6285 nan 0.0100 -0.0001
1060 0.6259 nan 0.0100 -0.0001
1080 0.6235 nan 0.0100 -0.0001
1100 0.6213 nan 0.0100 -0.0002
- Fold06.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2750 nan 0.1000 0.0310
2 1.2237 nan 0.1000 0.0235
3 1.1861 nan 0.1000 0.0202
4 1.1527 nan 0.1000 0.0168
5 1.1258 nan 0.1000 0.0138
6 1.1016 nan 0.1000 0.0121
7 1.0794 nan 0.1000 0.0096
8 1.0625 nan 0.1000 0.0076
9 1.0424 nan 0.1000 0.0068
10 1.0262 nan 0.1000 0.0056
20 0.9363 nan 0.1000 0.0019
40 0.8577 nan 0.1000 0.0010
60 0.8184 nan 0.1000 -0.0001
80 0.7937 nan 0.1000 0.0001
100 0.7780 nan 0.1000 -0.0013
120 0.7647 nan 0.1000 -0.0004
140 0.7529 nan 0.1000 -0.0007
160 0.7444 nan 0.1000 -0.0005
180 0.7384 nan 0.1000 -0.0003
200 0.7323 nan 0.1000 -0.0005
220 0.7255 nan 0.1000 -0.0016
240 0.7227 nan 0.1000 -0.0013
260 0.7184 nan 0.1000 -0.0003
280 0.7143 nan 0.1000 -0.0001
300 0.7106 nan 0.1000 -0.0007
320 0.7059 nan 0.1000 -0.0004
340 0.7030 nan 0.1000 -0.0013
360 0.6995 nan 0.1000 -0.0004
380 0.6965 nan 0.1000 -0.0001
400 0.6937 nan 0.1000 -0.0011
420 0.6885 nan 0.1000 -0.0010
440 0.6859 nan 0.1000 -0.0003
460 0.6842 nan 0.1000 -0.0009
480 0.6821 nan 0.1000 -0.0006
500 0.6795 nan 0.1000 -0.0006
520 0.6772 nan 0.1000 -0.0007
540 0.6765 nan 0.1000 -0.0007
560 0.6748 nan 0.1000 -0.0014
580 0.6731 nan 0.1000 -0.0005
600 0.6723 nan 0.1000 -0.0021
620 0.6696 nan 0.1000 -0.0007
640 0.6673 nan 0.1000 -0.0007
660 0.6657 nan 0.1000 -0.0005
680 0.6623 nan 0.1000 -0.0006
700 0.6605 nan 0.1000 -0.0007
720 0.6590 nan 0.1000 -0.0006
740 0.6579 nan 0.1000 -0.0010
760 0.6553 nan 0.1000 -0.0014
780 0.6531 nan 0.1000 -0.0015
800 0.6513 nan 0.1000 -0.0008
820 0.6497 nan 0.1000 -0.0004
840 0.6479 nan 0.1000 -0.0008
860 0.6445 nan 0.1000 -0.0004
880 0.6428 nan 0.1000 -0.0009
900 0.6432 nan 0.1000 -0.0008
920 0.6413 nan 0.1000 -0.0003
940 0.6397 nan 0.1000 -0.0006
960 0.6379 nan 0.1000 -0.0007
980 0.6355 nan 0.1000 -0.0008
1000 0.6344 nan 0.1000 -0.0005
1020 0.6344 nan 0.1000 -0.0019
1040 0.6325 nan 0.1000 -0.0008
1060 0.6324 nan 0.1000 -0.0013
1080 0.6314 nan 0.1000 -0.0002
1100 0.6308 nan 0.1000 -0.0007
- Fold06.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2570 nan 0.1000 0.0354
2 1.1900 nan 0.1000 0.0320
3 1.1375 nan 0.1000 0.0258
4 1.0923 nan 0.1000 0.0209
5 1.0581 nan 0.1000 0.0162
6 1.0313 nan 0.1000 0.0142
7 1.0059 nan 0.1000 0.0124
8 0.9819 nan 0.1000 0.0106
9 0.9633 nan 0.1000 0.0096
10 0.9471 nan 0.1000 0.0082
20 0.8485 nan 0.1000 0.0027
40 0.7762 nan 0.1000 0.0009
60 0.7410 nan 0.1000 -0.0006
80 0.7140 nan 0.1000 -0.0007
100 0.6942 nan 0.1000 -0.0012
120 0.6764 nan 0.1000 -0.0008
140 0.6573 nan 0.1000 -0.0017
160 0.6465 nan 0.1000 -0.0011
180 0.6327 nan 0.1000 -0.0001
200 0.6246 nan 0.1000 -0.0015
220 0.6099 nan 0.1000 -0.0011
240 0.5976 nan 0.1000 -0.0010
260 0.5871 nan 0.1000 -0.0023
280 0.5786 nan 0.1000 -0.0005
300 0.5695 nan 0.1000 -0.0010
320 0.5625 nan 0.1000 -0.0006
340 0.5551 nan 0.1000 -0.0012
360 0.5483 nan 0.1000 -0.0004
380 0.5403 nan 0.1000 -0.0021
400 0.5346 nan 0.1000 -0.0006
420 0.5301 nan 0.1000 -0.0009
440 0.5239 nan 0.1000 -0.0010
460 0.5189 nan 0.1000 -0.0005
480 0.5148 nan 0.1000 -0.0010
500 0.5054 nan 0.1000 -0.0016
520 0.4975 nan 0.1000 -0.0011
540 0.4919 nan 0.1000 -0.0014
560 0.4869 nan 0.1000 -0.0009
580 0.4820 nan 0.1000 -0.0005
600 0.4762 nan 0.1000 -0.0008
620 0.4712 nan 0.1000 -0.0011
640 0.4675 nan 0.1000 -0.0009
660 0.4639 nan 0.1000 -0.0017
680 0.4592 nan 0.1000 -0.0007
700 0.4534 nan 0.1000 -0.0008
720 0.4495 nan 0.1000 -0.0008
740 0.4451 nan 0.1000 -0.0006
760 0.4438 nan 0.1000 -0.0009
780 0.4392 nan 0.1000 -0.0007
800 0.4350 nan 0.1000 -0.0013
820 0.4303 nan 0.1000 -0.0007
840 0.4257 nan 0.1000 -0.0013
860 0.4237 nan 0.1000 -0.0007
880 0.4194 nan 0.1000 -0.0008
900 0.4164 nan 0.1000 -0.0011
920 0.4127 nan 0.1000 -0.0012
940 0.4099 nan 0.1000 -0.0006
960 0.4071 nan 0.1000 -0.0011
980 0.4040 nan 0.1000 -0.0008
1000 0.4007 nan 0.1000 -0.0006
1020 0.3969 nan 0.1000 -0.0010
1040 0.3949 nan 0.1000 -0.0008
1060 0.3926 nan 0.1000 -0.0015
1080 0.3895 nan 0.1000 -0.0015
1100 0.3865 nan 0.1000 -0.0006
- Fold06.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold06.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2514 nan 0.1000 0.0431
2 1.1840 nan 0.1000 0.0313
3 1.1300 nan 0.1000 0.0278
4 1.0863 nan 0.1000 0.0210
5 1.0469 nan 0.1000 0.0168
6 1.0100 nan 0.1000 0.0140
7 0.9814 nan 0.1000 0.0135
8 0.9536 nan 0.1000 0.0115
9 0.9327 nan 0.1000 0.0088
10 0.9149 nan 0.1000 0.0085
20 0.8125 nan 0.1000 0.0010
40 0.7408 nan 0.1000 0.0006
60 0.7019 nan 0.1000 -0.0009
80 0.6734 nan 0.1000 -0.0008
100 0.6429 nan 0.1000 -0.0005
120 0.6148 nan 0.1000 -0.0006
140 0.5924 nan 0.1000 -0.0011
160 0.5759 nan 0.1000 -0.0008
180 0.5589 nan 0.1000 -0.0007
200 0.5459 nan 0.1000 -0.0004
220 0.5332 nan 0.1000 -0.0014
240 0.5194 nan 0.1000 -0.0010
260 0.5085 nan 0.1000 -0.0010
280 0.4976 nan 0.1000 -0.0015
300 0.4855 nan 0.1000 -0.0010
320 0.4749 nan 0.1000 -0.0013
340 0.4615 nan 0.1000 -0.0014
360 0.4504 nan 0.1000 -0.0023
380 0.4406 nan 0.1000 -0.0004
400 0.4319 nan 0.1000 -0.0010
420 0.4232 nan 0.1000 -0.0006
440 0.4173 nan 0.1000 -0.0010
460 0.4057 nan 0.1000 -0.0011
480 0.3982 nan 0.1000 -0.0014
500 0.3923 nan 0.1000 -0.0009
520 0.3850 nan 0.1000 -0.0006
540 0.3775 nan 0.1000 -0.0005
560 0.3719 nan 0.1000 -0.0016
580 0.3671 nan 0.1000 -0.0013
600 0.3592 nan 0.1000 -0.0011
620 0.3528 nan 0.1000 -0.0008
640 0.3469 nan 0.1000 -0.0014
660 0.3409 nan 0.1000 -0.0022
680 0.3359 nan 0.1000 -0.0015
700 0.3313 nan 0.1000 -0.0011
720 0.3276 nan 0.1000 -0.0010
740 0.3216 nan 0.1000 -0.0014
760 0.3175 nan 0.1000 -0.0010
780 0.3135 nan 0.1000 -0.0008
800 0.3088 nan 0.1000 -0.0007
820 0.3040 nan 0.1000 -0.0003
840 0.2992 nan 0.1000 -0.0018
860 0.2940 nan 0.1000 -0.0008
880 0.2906 nan 0.1000 -0.0013
900 0.2869 nan 0.1000 -0.0007
920 0.2829 nan 0.1000 -0.0004
940 0.2795 nan 0.1000 -0.0005
960 0.2761 nan 0.1000 -0.0006
980 0.2729 nan 0.1000 -0.0007
1000 0.2698 nan 0.1000 -0.0005
1020 0.2675 nan 0.1000 -0.0014
1040 0.2629 nan 0.1000 -0.0005
1060 0.2610 nan 0.1000 -0.0003
1080 0.2571 nan 0.1000 -0.0006
1100 0.2549 nan 0.1000 -0.0003
- Fold06.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3266 nan 0.0100 0.0028
2 1.3206 nan 0.0100 0.0030
3 1.3141 nan 0.0100 0.0030
4 1.3088 nan 0.0100 0.0028
5 1.3031 nan 0.0100 0.0028
6 1.2979 nan 0.0100 0.0027
7 1.2928 nan 0.0100 0.0027
8 1.2876 nan 0.0100 0.0026
9 1.2826 nan 0.0100 0.0024
10 1.2774 nan 0.0100 0.0025
20 1.2321 nan 0.0100 0.0020
40 1.1623 nan 0.0100 0.0015
60 1.1135 nan 0.0100 0.0010
80 1.0762 nan 0.0100 0.0008
100 1.0454 nan 0.0100 0.0006
120 1.0200 nan 0.0100 0.0005
140 0.9998 nan 0.0100 0.0004
160 0.9814 nan 0.0100 0.0004
180 0.9659 nan 0.0100 0.0003
200 0.9521 nan 0.0100 0.0003
220 0.9407 nan 0.0100 0.0002
240 0.9305 nan 0.0100 0.0001
260 0.9214 nan 0.0100 0.0002
280 0.9133 nan 0.0100 0.0000
300 0.9051 nan 0.0100 0.0001
320 0.8981 nan 0.0100 0.0001
340 0.8918 nan 0.0100 0.0000
360 0.8858 nan 0.0100 0.0001
380 0.8807 nan 0.0100 0.0001
400 0.8755 nan 0.0100 0.0000
420 0.8707 nan 0.0100 0.0000
440 0.8662 nan 0.0100 -0.0000
460 0.8618 nan 0.0100 -0.0000
480 0.8576 nan 0.0100 0.0000
500 0.8538 nan 0.0100 0.0000
520 0.8502 nan 0.0100 0.0000
540 0.8469 nan 0.0100 -0.0000
560 0.8435 nan 0.0100 -0.0000
580 0.8405 nan 0.0100 -0.0000
600 0.8374 nan 0.0100 0.0000
620 0.8348 nan 0.0100 0.0000
640 0.8321 nan 0.0100 -0.0000
660 0.8294 nan 0.0100 0.0000
680 0.8270 nan 0.0100 -0.0000
700 0.8247 nan 0.0100 -0.0000
720 0.8222 nan 0.0100 -0.0000
740 0.8200 nan 0.0100 0.0000
760 0.8177 nan 0.0100 0.0000
780 0.8157 nan 0.0100 -0.0001
800 0.8137 nan 0.0100 -0.0000
820 0.8115 nan 0.0100 -0.0001
840 0.8096 nan 0.0100 -0.0001
860 0.8079 nan 0.0100 -0.0000
880 0.8059 nan 0.0100 -0.0000
900 0.8043 nan 0.0100 -0.0000
920 0.8028 nan 0.0100 0.0000
940 0.8010 nan 0.0100 -0.0000
960 0.7992 nan 0.0100 -0.0001
980 0.7979 nan 0.0100 -0.0000
1000 0.7964 nan 0.0100 -0.0000
1020 0.7949 nan 0.0100 -0.0001
1040 0.7933 nan 0.0100 0.0000
1060 0.7919 nan 0.0100 0.0000
1080 0.7905 nan 0.0100 -0.0000
1100 0.7892 nan 0.0100 -0.0001
- Fold07.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0036
2 1.3163 nan 0.0100 0.0035
3 1.3091 nan 0.0100 0.0033
4 1.3021 nan 0.0100 0.0037
5 1.2951 nan 0.0100 0.0032
6 1.2889 nan 0.0100 0.0032
7 1.2818 nan 0.0100 0.0030
8 1.2754 nan 0.0100 0.0030
9 1.2690 nan 0.0100 0.0033
10 1.2635 nan 0.0100 0.0027
20 1.2077 nan 0.0100 0.0024
40 1.1201 nan 0.0100 0.0019
60 1.0562 nan 0.0100 0.0013
80 1.0091 nan 0.0100 0.0009
100 0.9726 nan 0.0100 0.0008
120 0.9417 nan 0.0100 0.0005
140 0.9185 nan 0.0100 0.0005
160 0.9001 nan 0.0100 0.0004
180 0.8847 nan 0.0100 0.0003
200 0.8716 nan 0.0100 0.0001
220 0.8599 nan 0.0100 0.0001
240 0.8496 nan 0.0100 0.0002
260 0.8408 nan 0.0100 -0.0000
280 0.8327 nan 0.0100 0.0000
300 0.8256 nan 0.0100 -0.0000
320 0.8190 nan 0.0100 0.0000
340 0.8128 nan 0.0100 0.0001
360 0.8072 nan 0.0100 -0.0001
380 0.8014 nan 0.0100 -0.0000
400 0.7962 nan 0.0100 -0.0000
420 0.7918 nan 0.0100 -0.0000
440 0.7876 nan 0.0100 -0.0001
460 0.7834 nan 0.0100 0.0000
480 0.7797 nan 0.0100 -0.0001
500 0.7759 nan 0.0100 -0.0000
520 0.7719 nan 0.0100 0.0000
540 0.7684 nan 0.0100 -0.0001
560 0.7649 nan 0.0100 -0.0000
580 0.7619 nan 0.0100 0.0000
600 0.7588 nan 0.0100 -0.0001
620 0.7558 nan 0.0100 -0.0000
640 0.7528 nan 0.0100 -0.0000
660 0.7496 nan 0.0100 -0.0000
680 0.7472 nan 0.0100 -0.0001
700 0.7448 nan 0.0100 -0.0001
720 0.7423 nan 0.0100 -0.0001
740 0.7397 nan 0.0100 -0.0000
760 0.7373 nan 0.0100 -0.0001
780 0.7346 nan 0.0100 -0.0001
800 0.7324 nan 0.0100 -0.0000
820 0.7301 nan 0.0100 -0.0000
840 0.7279 nan 0.0100 -0.0001
860 0.7258 nan 0.0100 -0.0001
880 0.7239 nan 0.0100 -0.0001
900 0.7220 nan 0.0100 -0.0000
920 0.7202 nan 0.0100 -0.0001
940 0.7186 nan 0.0100 -0.0001
960 0.7167 nan 0.0100 -0.0001
980 0.7149 nan 0.0100 -0.0001
1000 0.7125 nan 0.0100 -0.0001
1020 0.7106 nan 0.0100 -0.0001
1040 0.7086 nan 0.0100 -0.0002
1060 0.7072 nan 0.0100 -0.0001
1080 0.7052 nan 0.0100 -0.0002
1100 0.7032 nan 0.0100 -0.0000
- Fold07.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3238 nan 0.0100 0.0041
2 1.3157 nan 0.0100 0.0041
3 1.3085 nan 0.0100 0.0037
4 1.3005 nan 0.0100 0.0038
5 1.2926 nan 0.0100 0.0040
6 1.2848 nan 0.0100 0.0034
7 1.2774 nan 0.0100 0.0035
8 1.2706 nan 0.0100 0.0035
9 1.2636 nan 0.0100 0.0032
10 1.2569 nan 0.0100 0.0035
20 1.1948 nan 0.0100 0.0026
40 1.0986 nan 0.0100 0.0019
60 1.0286 nan 0.0100 0.0013
80 0.9753 nan 0.0100 0.0010
100 0.9362 nan 0.0100 0.0006
120 0.9058 nan 0.0100 0.0005
140 0.8820 nan 0.0100 0.0006
160 0.8624 nan 0.0100 0.0001
180 0.8460 nan 0.0100 0.0004
200 0.8335 nan 0.0100 0.0001
220 0.8223 nan 0.0100 0.0000
240 0.8119 nan 0.0100 -0.0002
260 0.8022 nan 0.0100 0.0002
280 0.7930 nan 0.0100 0.0002
300 0.7853 nan 0.0100 -0.0001
320 0.7778 nan 0.0100 0.0001
340 0.7719 nan 0.0100 0.0001
360 0.7659 nan 0.0100 -0.0001
380 0.7599 nan 0.0100 -0.0001
400 0.7544 nan 0.0100 -0.0002
420 0.7495 nan 0.0100 0.0000
440 0.7450 nan 0.0100 -0.0001
460 0.7407 nan 0.0100 -0.0000
480 0.7366 nan 0.0100 -0.0001
500 0.7315 nan 0.0100 -0.0000
520 0.7276 nan 0.0100 -0.0001
540 0.7234 nan 0.0100 -0.0000
560 0.7196 nan 0.0100 -0.0000
580 0.7161 nan 0.0100 -0.0001
600 0.7120 nan 0.0100 -0.0001
620 0.7082 nan 0.0100 0.0000
640 0.7046 nan 0.0100 -0.0000
660 0.7014 nan 0.0100 -0.0001
680 0.6979 nan 0.0100 -0.0001
700 0.6949 nan 0.0100 -0.0001
720 0.6914 nan 0.0100 -0.0000
740 0.6882 nan 0.0100 -0.0002
760 0.6851 nan 0.0100 -0.0001
780 0.6821 nan 0.0100 -0.0002
800 0.6795 nan 0.0100 -0.0001
820 0.6763 nan 0.0100 -0.0000
840 0.6733 nan 0.0100 -0.0001
860 0.6702 nan 0.0100 0.0000
880 0.6682 nan 0.0100 -0.0001
900 0.6653 nan 0.0100 -0.0001
920 0.6630 nan 0.0100 -0.0002
940 0.6604 nan 0.0100 -0.0001
960 0.6580 nan 0.0100 -0.0001
980 0.6558 nan 0.0100 -0.0001
1000 0.6532 nan 0.0100 -0.0001
1020 0.6506 nan 0.0100 -0.0001
1040 0.6484 nan 0.0100 -0.0001
1060 0.6462 nan 0.0100 -0.0001
1080 0.6439 nan 0.0100 -0.0000
1100 0.6418 nan 0.0100 -0.0001
- Fold07.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2743 nan 0.1000 0.0287
2 1.2287 nan 0.1000 0.0237
3 1.1936 nan 0.1000 0.0189
4 1.1590 nan 0.1000 0.0143
5 1.1354 nan 0.1000 0.0124
6 1.1158 nan 0.1000 0.0084
7 1.0914 nan 0.1000 0.0108
8 1.0697 nan 0.1000 0.0083
9 1.0540 nan 0.1000 0.0075
10 1.0395 nan 0.1000 0.0066
20 0.9480 nan 0.1000 0.0021
40 0.8733 nan 0.1000 0.0011
60 0.8355 nan 0.1000 0.0002
80 0.8145 nan 0.1000 -0.0008
100 0.7987 nan 0.1000 -0.0000
120 0.7855 nan 0.1000 -0.0005
140 0.7762 nan 0.1000 -0.0004
160 0.7664 nan 0.1000 -0.0018
180 0.7598 nan 0.1000 -0.0012
200 0.7539 nan 0.1000 -0.0009
220 0.7471 nan 0.1000 -0.0004
240 0.7412 nan 0.1000 -0.0007
260 0.7354 nan 0.1000 -0.0006
280 0.7308 nan 0.1000 -0.0010
300 0.7252 nan 0.1000 -0.0010
320 0.7218 nan 0.1000 -0.0022
340 0.7180 nan 0.1000 -0.0007
360 0.7133 nan 0.1000 -0.0003
380 0.7096 nan 0.1000 -0.0008
400 0.7066 nan 0.1000 -0.0005
420 0.7051 nan 0.1000 -0.0009
440 0.7017 nan 0.1000 -0.0007
460 0.6986 nan 0.1000 -0.0009
480 0.6950 nan 0.1000 -0.0020
500 0.6936 nan 0.1000 -0.0006
520 0.6905 nan 0.1000 -0.0010
540 0.6883 nan 0.1000 -0.0004
560 0.6857 nan 0.1000 -0.0008
580 0.6833 nan 0.1000 -0.0005
600 0.6807 nan 0.1000 -0.0005
620 0.6786 nan 0.1000 -0.0016
640 0.6765 nan 0.1000 -0.0005
660 0.6754 nan 0.1000 -0.0014
680 0.6736 nan 0.1000 -0.0008
700 0.6718 nan 0.1000 -0.0009
720 0.6707 nan 0.1000 -0.0016
740 0.6680 nan 0.1000 -0.0005
760 0.6663 nan 0.1000 -0.0013
780 0.6649 nan 0.1000 -0.0003
800 0.6639 nan 0.1000 -0.0003
820 0.6623 nan 0.1000 -0.0006
840 0.6606 nan 0.1000 -0.0007
860 0.6601 nan 0.1000 -0.0008
880 0.6579 nan 0.1000 -0.0006
900 0.6570 nan 0.1000 -0.0010
920 0.6558 nan 0.1000 -0.0003
940 0.6543 nan 0.1000 -0.0003
960 0.6529 nan 0.1000 -0.0005
980 0.6500 nan 0.1000 -0.0005
1000 0.6487 nan 0.1000 -0.0004
1020 0.6472 nan 0.1000 -0.0008
1040 0.6454 nan 0.1000 -0.0003
1060 0.6441 nan 0.1000 -0.0009
1080 0.6424 nan 0.1000 -0.0012
1100 0.6414 nan 0.1000 -0.0005
- Fold07.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2661 nan 0.1000 0.0357
2 1.2073 nan 0.1000 0.0293
3 1.1583 nan 0.1000 0.0228
4 1.1172 nan 0.1000 0.0218
5 1.0794 nan 0.1000 0.0166
6 1.0472 nan 0.1000 0.0125
7 1.0248 nan 0.1000 0.0101
8 1.0029 nan 0.1000 0.0099
9 0.9831 nan 0.1000 0.0081
10 0.9672 nan 0.1000 0.0064
20 0.8675 nan 0.1000 0.0027
40 0.7957 nan 0.1000 0.0013
60 0.7596 nan 0.1000 -0.0005
80 0.7362 nan 0.1000 -0.0014
100 0.7206 nan 0.1000 -0.0011
120 0.7007 nan 0.1000 -0.0011
140 0.6844 nan 0.1000 -0.0004
160 0.6710 nan 0.1000 -0.0007
180 0.6565 nan 0.1000 -0.0004
200 0.6464 nan 0.1000 -0.0008
220 0.6334 nan 0.1000 -0.0010
240 0.6217 nan 0.1000 -0.0006
260 0.6100 nan 0.1000 -0.0008
280 0.6017 nan 0.1000 -0.0014
300 0.5914 nan 0.1000 -0.0005
320 0.5788 nan 0.1000 0.0002
340 0.5695 nan 0.1000 -0.0017
360 0.5634 nan 0.1000 -0.0010
380 0.5571 nan 0.1000 -0.0018
400 0.5498 nan 0.1000 -0.0013
420 0.5422 nan 0.1000 -0.0004
440 0.5367 nan 0.1000 -0.0008
460 0.5320 nan 0.1000 -0.0005
480 0.5269 nan 0.1000 -0.0020
500 0.5194 nan 0.1000 -0.0008
520 0.5120 nan 0.1000 -0.0010
540 0.5077 nan 0.1000 -0.0007
560 0.5024 nan 0.1000 -0.0007
580 0.4973 nan 0.1000 -0.0002
600 0.4914 nan 0.1000 -0.0012
620 0.4858 nan 0.1000 -0.0008
640 0.4818 nan 0.1000 -0.0006
660 0.4767 nan 0.1000 -0.0013
680 0.4713 nan 0.1000 -0.0011
700 0.4664 nan 0.1000 -0.0007
720 0.4624 nan 0.1000 -0.0012
740 0.4582 nan 0.1000 -0.0009
760 0.4529 nan 0.1000 -0.0009
780 0.4489 nan 0.1000 -0.0017
800 0.4462 nan 0.1000 -0.0007
820 0.4443 nan 0.1000 -0.0011
840 0.4410 nan 0.1000 -0.0008
860 0.4368 nan 0.1000 -0.0011
880 0.4329 nan 0.1000 -0.0006
900 0.4309 nan 0.1000 -0.0009
920 0.4274 nan 0.1000 -0.0008
940 0.4233 nan 0.1000 -0.0006
960 0.4190 nan 0.1000 -0.0006
980 0.4149 nan 0.1000 -0.0006
1000 0.4105 nan 0.1000 -0.0012
1020 0.4064 nan 0.1000 -0.0009
1040 0.4020 nan 0.1000 -0.0019
1060 0.4001 nan 0.1000 -0.0004
1080 0.3963 nan 0.1000 -0.0005
1100 0.3949 nan 0.1000 -0.0010
- Fold07.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold07.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2552 nan 0.1000 0.0407
2 1.1879 nan 0.1000 0.0323
3 1.1323 nan 0.1000 0.0262
4 1.0867 nan 0.1000 0.0217
5 1.0479 nan 0.1000 0.0165
6 1.0204 nan 0.1000 0.0127
7 0.9959 nan 0.1000 0.0124
8 0.9729 nan 0.1000 0.0095
9 0.9526 nan 0.1000 0.0073
10 0.9355 nan 0.1000 0.0057
20 0.8328 nan 0.1000 0.0016
40 0.7545 nan 0.1000 -0.0006
60 0.7135 nan 0.1000 -0.0002
80 0.6830 nan 0.1000 -0.0021
100 0.6587 nan 0.1000 -0.0008
120 0.6381 nan 0.1000 -0.0007
140 0.6176 nan 0.1000 -0.0013
160 0.5974 nan 0.1000 -0.0007
180 0.5842 nan 0.1000 -0.0022
200 0.5665 nan 0.1000 -0.0021
220 0.5531 nan 0.1000 -0.0007
240 0.5389 nan 0.1000 -0.0012
260 0.5292 nan 0.1000 -0.0014
280 0.5176 nan 0.1000 -0.0006
300 0.5054 nan 0.1000 -0.0013
320 0.4969 nan 0.1000 -0.0011
340 0.4866 nan 0.1000 -0.0014
360 0.4781 nan 0.1000 -0.0010
380 0.4663 nan 0.1000 -0.0013
400 0.4590 nan 0.1000 -0.0017
420 0.4488 nan 0.1000 -0.0009
440 0.4419 nan 0.1000 -0.0013
460 0.4331 nan 0.1000 -0.0016
480 0.4249 nan 0.1000 -0.0012
500 0.4180 nan 0.1000 -0.0003
520 0.4085 nan 0.1000 -0.0008
540 0.4028 nan 0.1000 -0.0013
560 0.3954 nan 0.1000 -0.0009
580 0.3876 nan 0.1000 -0.0008
600 0.3824 nan 0.1000 -0.0012
620 0.3727 nan 0.1000 -0.0011
640 0.3683 nan 0.1000 -0.0009
660 0.3621 nan 0.1000 -0.0007
680 0.3563 nan 0.1000 -0.0006
700 0.3501 nan 0.1000 -0.0007
720 0.3453 nan 0.1000 -0.0005
740 0.3406 nan 0.1000 -0.0008
760 0.3366 nan 0.1000 -0.0007
780 0.3331 nan 0.1000 -0.0010
800 0.3280 nan 0.1000 -0.0007
820 0.3247 nan 0.1000 -0.0006
840 0.3208 nan 0.1000 -0.0011
860 0.3161 nan 0.1000 -0.0014
880 0.3104 nan 0.1000 -0.0009
900 0.3053 nan 0.1000 -0.0007
920 0.3014 nan 0.1000 -0.0008
940 0.2966 nan 0.1000 -0.0006
960 0.2927 nan 0.1000 -0.0004
980 0.2878 nan 0.1000 -0.0008
1000 0.2848 nan 0.1000 -0.0011
1020 0.2805 nan 0.1000 -0.0011
1040 0.2762 nan 0.1000 -0.0013
1060 0.2738 nan 0.1000 -0.0008
1080 0.2704 nan 0.1000 -0.0006
1100 0.2671 nan 0.1000 -0.0005
- Fold07.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3263 nan 0.0100 0.0029
2 1.3204 nan 0.0100 0.0028
3 1.3148 nan 0.0100 0.0027
4 1.3091 nan 0.0100 0.0027
5 1.3038 nan 0.0100 0.0026
6 1.2984 nan 0.0100 0.0025
7 1.2932 nan 0.0100 0.0025
8 1.2880 nan 0.0100 0.0025
9 1.2828 nan 0.0100 0.0024
10 1.2781 nan 0.0100 0.0024
20 1.2334 nan 0.0100 0.0019
40 1.1663 nan 0.0100 0.0013
60 1.1192 nan 0.0100 0.0008
80 1.0834 nan 0.0100 0.0008
100 1.0542 nan 0.0100 0.0006
120 1.0299 nan 0.0100 0.0005
140 1.0096 nan 0.0100 0.0004
160 0.9937 nan 0.0100 0.0003
180 0.9790 nan 0.0100 0.0003
200 0.9653 nan 0.0100 0.0002
220 0.9540 nan 0.0100 0.0000
240 0.9430 nan 0.0100 0.0001
260 0.9337 nan 0.0100 0.0001
280 0.9255 nan 0.0100 0.0001
300 0.9174 nan 0.0100 0.0000
320 0.9106 nan 0.0100 0.0001
340 0.9046 nan 0.0100 0.0000
360 0.8988 nan 0.0100 0.0001
380 0.8928 nan 0.0100 0.0001
400 0.8875 nan 0.0100 0.0000
420 0.8823 nan 0.0100 0.0001
440 0.8778 nan 0.0100 0.0000
460 0.8735 nan 0.0100 0.0001
480 0.8698 nan 0.0100 -0.0000
500 0.8656 nan 0.0100 -0.0000
520 0.8621 nan 0.0100 0.0000
540 0.8585 nan 0.0100 0.0000
560 0.8551 nan 0.0100 0.0000
580 0.8518 nan 0.0100 -0.0000
600 0.8486 nan 0.0100 0.0000
620 0.8457 nan 0.0100 0.0000
640 0.8427 nan 0.0100 -0.0000
660 0.8399 nan 0.0100 0.0000
680 0.8373 nan 0.0100 -0.0001
700 0.8348 nan 0.0100 -0.0000
720 0.8327 nan 0.0100 -0.0000
740 0.8302 nan 0.0100 -0.0000
760 0.8279 nan 0.0100 -0.0001
780 0.8258 nan 0.0100 -0.0000
800 0.8235 nan 0.0100 -0.0000
820 0.8217 nan 0.0100 -0.0001
840 0.8197 nan 0.0100 -0.0001
860 0.8177 nan 0.0100 -0.0000
880 0.8158 nan 0.0100 -0.0000
900 0.8144 nan 0.0100 -0.0001
920 0.8126 nan 0.0100 -0.0000
940 0.8110 nan 0.0100 -0.0001
960 0.8092 nan 0.0100 -0.0000
980 0.8075 nan 0.0100 -0.0000
1000 0.8059 nan 0.0100 -0.0000
1020 0.8045 nan 0.0100 0.0000
1040 0.8032 nan 0.0100 -0.0001
1060 0.8019 nan 0.0100 -0.0001
1080 0.8005 nan 0.0100 -0.0000
1100 0.7993 nan 0.0100 -0.0000
- Fold08.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3251 nan 0.0100 0.0036
2 1.3183 nan 0.0100 0.0035
3 1.3124 nan 0.0100 0.0029
4 1.3056 nan 0.0100 0.0032
5 1.2985 nan 0.0100 0.0033
6 1.2922 nan 0.0100 0.0034
7 1.2860 nan 0.0100 0.0032
8 1.2800 nan 0.0100 0.0029
9 1.2736 nan 0.0100 0.0030
10 1.2674 nan 0.0100 0.0030
20 1.2114 nan 0.0100 0.0025
40 1.1265 nan 0.0100 0.0019
60 1.0640 nan 0.0100 0.0012
80 1.0163 nan 0.0100 0.0008
100 0.9796 nan 0.0100 0.0005
120 0.9516 nan 0.0100 0.0002
140 0.9293 nan 0.0100 0.0003
160 0.9114 nan 0.0100 0.0003
180 0.8956 nan 0.0100 0.0002
200 0.8830 nan 0.0100 0.0002
220 0.8713 nan 0.0100 -0.0000
240 0.8614 nan 0.0100 0.0001
260 0.8514 nan 0.0100 0.0002
280 0.8435 nan 0.0100 0.0001
300 0.8357 nan 0.0100 0.0001
320 0.8290 nan 0.0100 0.0000
340 0.8227 nan 0.0100 -0.0000
360 0.8171 nan 0.0100 -0.0000
380 0.8118 nan 0.0100 -0.0000
400 0.8064 nan 0.0100 0.0000
420 0.8020 nan 0.0100 0.0000
440 0.7974 nan 0.0100 -0.0000
460 0.7925 nan 0.0100 -0.0000
480 0.7886 nan 0.0100 -0.0001
500 0.7848 nan 0.0100 -0.0001
520 0.7809 nan 0.0100 -0.0000
540 0.7775 nan 0.0100 -0.0000
560 0.7736 nan 0.0100 -0.0000
580 0.7700 nan 0.0100 -0.0001
600 0.7667 nan 0.0100 -0.0001
620 0.7640 nan 0.0100 -0.0001
640 0.7611 nan 0.0100 -0.0001
660 0.7587 nan 0.0100 -0.0000
680 0.7561 nan 0.0100 -0.0001
700 0.7536 nan 0.0100 -0.0001
720 0.7510 nan 0.0100 -0.0001
740 0.7484 nan 0.0100 -0.0002
760 0.7455 nan 0.0100 -0.0001
780 0.7430 nan 0.0100 -0.0001
800 0.7405 nan 0.0100 -0.0001
820 0.7383 nan 0.0100 -0.0000
840 0.7359 nan 0.0100 -0.0001
860 0.7331 nan 0.0100 -0.0001
880 0.7309 nan 0.0100 -0.0000
900 0.7289 nan 0.0100 -0.0001
920 0.7269 nan 0.0100 0.0001
940 0.7246 nan 0.0100 -0.0000
960 0.7232 nan 0.0100 -0.0001
980 0.7215 nan 0.0100 -0.0001
1000 0.7197 nan 0.0100 -0.0001
1020 0.7180 nan 0.0100 -0.0001
1040 0.7160 nan 0.0100 -0.0001
1060 0.7143 nan 0.0100 -0.0001
1080 0.7123 nan 0.0100 -0.0001
1100 0.7101 nan 0.0100 -0.0000
- Fold08.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3240 nan 0.0100 0.0036
2 1.3166 nan 0.0100 0.0037
3 1.3088 nan 0.0100 0.0038
4 1.3015 nan 0.0100 0.0035
5 1.2943 nan 0.0100 0.0036
6 1.2874 nan 0.0100 0.0036
7 1.2807 nan 0.0100 0.0030
8 1.2737 nan 0.0100 0.0034
9 1.2668 nan 0.0100 0.0031
10 1.2594 nan 0.0100 0.0033
20 1.1991 nan 0.0100 0.0028
40 1.1039 nan 0.0100 0.0020
60 1.0336 nan 0.0100 0.0013
80 0.9821 nan 0.0100 0.0007
100 0.9452 nan 0.0100 0.0007
120 0.9163 nan 0.0100 0.0005
140 0.8938 nan 0.0100 0.0002
160 0.8741 nan 0.0100 0.0003
180 0.8586 nan 0.0100 0.0004
200 0.8453 nan 0.0100 0.0002
220 0.8328 nan 0.0100 0.0001
240 0.8226 nan 0.0100 0.0001
260 0.8123 nan 0.0100 0.0003
280 0.8028 nan 0.0100 0.0001
300 0.7942 nan 0.0100 0.0000
320 0.7860 nan 0.0100 -0.0001
340 0.7787 nan 0.0100 0.0001
360 0.7723 nan 0.0100 0.0000
380 0.7669 nan 0.0100 -0.0001
400 0.7613 nan 0.0100 -0.0000
420 0.7562 nan 0.0100 -0.0001
440 0.7515 nan 0.0100 0.0000
460 0.7465 nan 0.0100 -0.0001
480 0.7419 nan 0.0100 -0.0001
500 0.7374 nan 0.0100 -0.0001
520 0.7332 nan 0.0100 0.0000
540 0.7291 nan 0.0100 -0.0001
560 0.7250 nan 0.0100 -0.0001
580 0.7210 nan 0.0100 0.0000
600 0.7175 nan 0.0100 -0.0000
620 0.7133 nan 0.0100 -0.0000
640 0.7098 nan 0.0100 -0.0001
660 0.7064 nan 0.0100 -0.0001
680 0.7024 nan 0.0100 -0.0000
700 0.6992 nan 0.0100 -0.0001
720 0.6964 nan 0.0100 -0.0001
740 0.6935 nan 0.0100 -0.0001
760 0.6907 nan 0.0100 -0.0002
780 0.6877 nan 0.0100 -0.0000
800 0.6847 nan 0.0100 -0.0001
820 0.6818 nan 0.0100 -0.0001
840 0.6789 nan 0.0100 -0.0001
860 0.6760 nan 0.0100 0.0000
880 0.6733 nan 0.0100 -0.0000
900 0.6709 nan 0.0100 -0.0002
920 0.6687 nan 0.0100 -0.0001
940 0.6659 nan 0.0100 -0.0001
960 0.6631 nan 0.0100 -0.0000
980 0.6609 nan 0.0100 -0.0001
1000 0.6587 nan 0.0100 -0.0002
1020 0.6561 nan 0.0100 -0.0001
1040 0.6538 nan 0.0100 -0.0002
1060 0.6508 nan 0.0100 -0.0001
1080 0.6481 nan 0.0100 -0.0000
1100 0.6458 nan 0.0100 -0.0000
- Fold08.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2758 nan 0.1000 0.0281
2 1.2298 nan 0.1000 0.0211
3 1.1889 nan 0.1000 0.0182
4 1.1573 nan 0.1000 0.0149
5 1.1323 nan 0.1000 0.0123
6 1.1143 nan 0.1000 0.0072
7 1.0934 nan 0.1000 0.0095
8 1.0769 nan 0.1000 0.0075
9 1.0636 nan 0.1000 0.0070
10 1.0485 nan 0.1000 0.0074
20 0.9632 nan 0.1000 0.0021
40 0.8843 nan 0.1000 0.0007
60 0.8502 nan 0.1000 -0.0002
80 0.8229 nan 0.1000 -0.0010
100 0.8040 nan 0.1000 -0.0005
120 0.7911 nan 0.1000 0.0000
140 0.7817 nan 0.1000 -0.0006
160 0.7723 nan 0.1000 -0.0006
180 0.7650 nan 0.1000 -0.0002
200 0.7593 nan 0.1000 -0.0003
220 0.7522 nan 0.1000 -0.0006
240 0.7468 nan 0.1000 -0.0011
260 0.7416 nan 0.1000 -0.0000
280 0.7365 nan 0.1000 0.0000
300 0.7318 nan 0.1000 -0.0012
320 0.7294 nan 0.1000 -0.0011
340 0.7256 nan 0.1000 -0.0007
360 0.7221 nan 0.1000 -0.0006
380 0.7203 nan 0.1000 -0.0006
400 0.7172 nan 0.1000 -0.0002
420 0.7133 nan 0.1000 -0.0003
440 0.7105 nan 0.1000 -0.0011
460 0.7089 nan 0.1000 -0.0015
480 0.7059 nan 0.1000 -0.0012
500 0.7023 nan 0.1000 -0.0006
520 0.6998 nan 0.1000 -0.0004
540 0.6977 nan 0.1000 -0.0011
560 0.6955 nan 0.1000 -0.0005
580 0.6931 nan 0.1000 -0.0008
600 0.6919 nan 0.1000 -0.0001
620 0.6898 nan 0.1000 -0.0005
640 0.6869 nan 0.1000 -0.0004
660 0.6841 nan 0.1000 -0.0006
680 0.6833 nan 0.1000 -0.0007
700 0.6818 nan 0.1000 -0.0012
720 0.6801 nan 0.1000 -0.0015
740 0.6793 nan 0.1000 -0.0009
760 0.6789 nan 0.1000 -0.0010
780 0.6766 nan 0.1000 -0.0010
800 0.6737 nan 0.1000 -0.0007
820 0.6727 nan 0.1000 -0.0010
840 0.6707 nan 0.1000 -0.0009
860 0.6689 nan 0.1000 -0.0006
880 0.6676 nan 0.1000 -0.0005
900 0.6665 nan 0.1000 -0.0012
920 0.6642 nan 0.1000 -0.0004
940 0.6620 nan 0.1000 -0.0009
960 0.6604 nan 0.1000 -0.0007
980 0.6594 nan 0.1000 -0.0008
1000 0.6573 nan 0.1000 -0.0010
1020 0.6558 nan 0.1000 -0.0009
1040 0.6553 nan 0.1000 -0.0011
1060 0.6537 nan 0.1000 -0.0010
1080 0.6525 nan 0.1000 -0.0005
1100 0.6516 nan 0.1000 -0.0010
- Fold08.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2651 nan 0.1000 0.0297
2 1.2046 nan 0.1000 0.0281
3 1.1562 nan 0.1000 0.0239
4 1.1209 nan 0.1000 0.0186
5 1.0814 nan 0.1000 0.0185
6 1.0544 nan 0.1000 0.0112
7 1.0271 nan 0.1000 0.0121
8 1.0046 nan 0.1000 0.0102
9 0.9877 nan 0.1000 0.0084
10 0.9723 nan 0.1000 0.0070
20 0.8788 nan 0.1000 0.0016
40 0.8048 nan 0.1000 -0.0010
60 0.7654 nan 0.1000 -0.0004
80 0.7364 nan 0.1000 -0.0011
100 0.7187 nan 0.1000 -0.0008
120 0.7007 nan 0.1000 -0.0008
140 0.6870 nan 0.1000 -0.0009
160 0.6748 nan 0.1000 -0.0015
180 0.6621 nan 0.1000 -0.0003
200 0.6493 nan 0.1000 -0.0010
220 0.6360 nan 0.1000 -0.0005
240 0.6258 nan 0.1000 -0.0017
260 0.6146 nan 0.1000 -0.0007
280 0.6036 nan 0.1000 -0.0002
300 0.5939 nan 0.1000 -0.0011
320 0.5882 nan 0.1000 -0.0006
340 0.5780 nan 0.1000 -0.0005
360 0.5705 nan 0.1000 -0.0007
380 0.5633 nan 0.1000 -0.0006
400 0.5566 nan 0.1000 -0.0006
420 0.5509 nan 0.1000 -0.0006
440 0.5429 nan 0.1000 -0.0007
460 0.5350 nan 0.1000 -0.0008
480 0.5305 nan 0.1000 -0.0010
500 0.5266 nan 0.1000 -0.0007
520 0.5196 nan 0.1000 -0.0003
540 0.5155 nan 0.1000 -0.0007
560 0.5093 nan 0.1000 -0.0011
580 0.5025 nan 0.1000 -0.0002
600 0.4984 nan 0.1000 -0.0008
620 0.4910 nan 0.1000 -0.0003
640 0.4858 nan 0.1000 -0.0011
660 0.4826 nan 0.1000 -0.0010
680 0.4803 nan 0.1000 -0.0010
700 0.4780 nan 0.1000 -0.0011
720 0.4736 nan 0.1000 -0.0003
740 0.4677 nan 0.1000 -0.0014
760 0.4632 nan 0.1000 -0.0008
780 0.4582 nan 0.1000 -0.0012
800 0.4545 nan 0.1000 -0.0009
820 0.4517 nan 0.1000 -0.0013
840 0.4486 nan 0.1000 -0.0012
860 0.4460 nan 0.1000 -0.0008
880 0.4414 nan 0.1000 -0.0006
900 0.4362 nan 0.1000 -0.0013
920 0.4321 nan 0.1000 -0.0004
940 0.4294 nan 0.1000 -0.0010
960 0.4263 nan 0.1000 -0.0015
980 0.4213 nan 0.1000 -0.0004
1000 0.4183 nan 0.1000 -0.0010
1020 0.4150 nan 0.1000 -0.0004
1040 0.4116 nan 0.1000 -0.0006
1060 0.4084 nan 0.1000 -0.0004
1080 0.4053 nan 0.1000 -0.0007
1100 0.4031 nan 0.1000 -0.0013
- Fold08.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold08.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2588 nan 0.1000 0.0362
2 1.1931 nan 0.1000 0.0318
3 1.1407 nan 0.1000 0.0238
4 1.0947 nan 0.1000 0.0228
5 1.0563 nan 0.1000 0.0188
6 1.0208 nan 0.1000 0.0131
7 0.9905 nan 0.1000 0.0107
8 0.9703 nan 0.1000 0.0088
9 0.9500 nan 0.1000 0.0087
10 0.9329 nan 0.1000 0.0076
20 0.8385 nan 0.1000 0.0006
40 0.7623 nan 0.1000 0.0014
60 0.7193 nan 0.1000 -0.0019
80 0.6883 nan 0.1000 -0.0011
100 0.6605 nan 0.1000 -0.0010
120 0.6305 nan 0.1000 -0.0006
140 0.6119 nan 0.1000 -0.0008
160 0.5947 nan 0.1000 -0.0011
180 0.5791 nan 0.1000 -0.0010
200 0.5589 nan 0.1000 -0.0016
220 0.5424 nan 0.1000 -0.0011
240 0.5285 nan 0.1000 -0.0010
260 0.5164 nan 0.1000 -0.0007
280 0.5017 nan 0.1000 -0.0009
300 0.4873 nan 0.1000 -0.0008
320 0.4769 nan 0.1000 -0.0003
340 0.4663 nan 0.1000 -0.0008
360 0.4557 nan 0.1000 -0.0015
380 0.4469 nan 0.1000 -0.0013
400 0.4368 nan 0.1000 -0.0010
420 0.4300 nan 0.1000 -0.0013
440 0.4220 nan 0.1000 -0.0008
460 0.4143 nan 0.1000 -0.0010
480 0.4076 nan 0.1000 -0.0006
500 0.4005 nan 0.1000 -0.0012
520 0.3915 nan 0.1000 -0.0010
540 0.3853 nan 0.1000 -0.0008
560 0.3781 nan 0.1000 -0.0008
580 0.3718 nan 0.1000 -0.0007
600 0.3687 nan 0.1000 -0.0011
620 0.3608 nan 0.1000 -0.0012
640 0.3551 nan 0.1000 -0.0011
660 0.3485 nan 0.1000 -0.0008
680 0.3432 nan 0.1000 -0.0008
700 0.3374 nan 0.1000 -0.0004
720 0.3328 nan 0.1000 -0.0008
740 0.3295 nan 0.1000 -0.0010
760 0.3244 nan 0.1000 -0.0003
780 0.3195 nan 0.1000 -0.0006
800 0.3153 nan 0.1000 -0.0005
820 0.3110 nan 0.1000 -0.0011
840 0.3053 nan 0.1000 -0.0010
860 0.3007 nan 0.1000 -0.0007
880 0.2966 nan 0.1000 -0.0006
900 0.2916 nan 0.1000 -0.0013
920 0.2890 nan 0.1000 -0.0009
940 0.2860 nan 0.1000 -0.0011
960 0.2806 nan 0.1000 -0.0010
980 0.2780 nan 0.1000 -0.0015
1000 0.2748 nan 0.1000 -0.0008
1020 0.2708 nan 0.1000 -0.0011
1040 0.2663 nan 0.1000 -0.0011
1060 0.2634 nan 0.1000 -0.0006
1080 0.2601 nan 0.1000 -0.0004
1100 0.2573 nan 0.1000 -0.0012
- Fold08.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3260 nan 0.0100 0.0030
2 1.3202 nan 0.0100 0.0029
3 1.3147 nan 0.0100 0.0028
4 1.3092 nan 0.0100 0.0028
5 1.3038 nan 0.0100 0.0027
6 1.2985 nan 0.0100 0.0027
7 1.2932 nan 0.0100 0.0026
8 1.2880 nan 0.0100 0.0025
9 1.2827 nan 0.0100 0.0025
10 1.2773 nan 0.0100 0.0025
20 1.2337 nan 0.0100 0.0020
40 1.1660 nan 0.0100 0.0014
60 1.1179 nan 0.0100 0.0011
80 1.0803 nan 0.0100 0.0008
100 1.0493 nan 0.0100 0.0007
120 1.0231 nan 0.0100 0.0005
140 1.0018 nan 0.0100 0.0004
160 0.9836 nan 0.0100 0.0003
180 0.9670 nan 0.0100 0.0002
200 0.9528 nan 0.0100 0.0003
220 0.9405 nan 0.0100 0.0002
240 0.9290 nan 0.0100 0.0002
260 0.9187 nan 0.0100 0.0001
280 0.9100 nan 0.0100 0.0001
300 0.9015 nan 0.0100 0.0002
320 0.8940 nan 0.0100 0.0001
340 0.8870 nan 0.0100 0.0001
360 0.8810 nan 0.0100 0.0001
380 0.8751 nan 0.0100 0.0000
400 0.8700 nan 0.0100 0.0001
420 0.8652 nan 0.0100 -0.0001
440 0.8604 nan 0.0100 0.0001
460 0.8564 nan 0.0100 0.0000
480 0.8522 nan 0.0100 0.0000
500 0.8484 nan 0.0100 0.0000
520 0.8444 nan 0.0100 0.0000
540 0.8409 nan 0.0100 0.0000
560 0.8376 nan 0.0100 0.0000
580 0.8343 nan 0.0100 0.0000
600 0.8313 nan 0.0100 0.0000
620 0.8284 nan 0.0100 -0.0000
640 0.8258 nan 0.0100 -0.0000
660 0.8231 nan 0.0100 -0.0000
680 0.8205 nan 0.0100 -0.0000
700 0.8177 nan 0.0100 0.0000
720 0.8153 nan 0.0100 -0.0001
740 0.8129 nan 0.0100 -0.0000
760 0.8107 nan 0.0100 -0.0001
780 0.8087 nan 0.0100 -0.0000
800 0.8064 nan 0.0100 -0.0001
820 0.8045 nan 0.0100 0.0000
840 0.8026 nan 0.0100 0.0000
860 0.8006 nan 0.0100 -0.0001
880 0.7991 nan 0.0100 -0.0001
900 0.7972 nan 0.0100 0.0000
920 0.7952 nan 0.0100 -0.0000
940 0.7936 nan 0.0100 -0.0000
960 0.7916 nan 0.0100 -0.0000
980 0.7902 nan 0.0100 0.0000
1000 0.7886 nan 0.0100 -0.0000
1020 0.7870 nan 0.0100 0.0000
1040 0.7855 nan 0.0100 -0.0000
1060 0.7841 nan 0.0100 -0.0000
1080 0.7827 nan 0.0100 -0.0000
1100 0.7814 nan 0.0100 -0.0001
- Fold09.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0037
2 1.3164 nan 0.0100 0.0034
3 1.3091 nan 0.0100 0.0035
4 1.3017 nan 0.0100 0.0035
5 1.2952 nan 0.0100 0.0035
6 1.2886 nan 0.0100 0.0032
7 1.2825 nan 0.0100 0.0033
8 1.2763 nan 0.0100 0.0032
9 1.2696 nan 0.0100 0.0031
10 1.2635 nan 0.0100 0.0031
20 1.2071 nan 0.0100 0.0027
40 1.1168 nan 0.0100 0.0016
60 1.0511 nan 0.0100 0.0014
80 1.0015 nan 0.0100 0.0010
100 0.9637 nan 0.0100 0.0008
120 0.9335 nan 0.0100 0.0006
140 0.9111 nan 0.0100 0.0004
160 0.8925 nan 0.0100 0.0004
180 0.8773 nan 0.0100 0.0001
200 0.8640 nan 0.0100 0.0002
220 0.8532 nan 0.0100 0.0000
240 0.8429 nan 0.0100 0.0001
260 0.8336 nan 0.0100 0.0001
280 0.8256 nan 0.0100 -0.0001
300 0.8178 nan 0.0100 -0.0000
320 0.8105 nan 0.0100 0.0002
340 0.8035 nan 0.0100 -0.0000
360 0.7980 nan 0.0100 -0.0000
380 0.7926 nan 0.0100 0.0001
400 0.7875 nan 0.0100 -0.0000
420 0.7824 nan 0.0100 -0.0000
440 0.7779 nan 0.0100 -0.0001
460 0.7728 nan 0.0100 -0.0001
480 0.7683 nan 0.0100 -0.0002
500 0.7645 nan 0.0100 -0.0001
520 0.7608 nan 0.0100 -0.0001
540 0.7574 nan 0.0100 -0.0002
560 0.7539 nan 0.0100 -0.0000
580 0.7507 nan 0.0100 -0.0001
600 0.7477 nan 0.0100 -0.0000
620 0.7445 nan 0.0100 0.0000
640 0.7417 nan 0.0100 -0.0000
660 0.7385 nan 0.0100 -0.0001
680 0.7356 nan 0.0100 -0.0001
700 0.7331 nan 0.0100 -0.0001
720 0.7303 nan 0.0100 -0.0001
740 0.7280 nan 0.0100 -0.0001
760 0.7257 nan 0.0100 -0.0001
780 0.7228 nan 0.0100 -0.0001
800 0.7203 nan 0.0100 -0.0001
820 0.7179 nan 0.0100 -0.0001
840 0.7154 nan 0.0100 -0.0001
860 0.7132 nan 0.0100 -0.0001
880 0.7107 nan 0.0100 0.0000
900 0.7089 nan 0.0100 -0.0001
920 0.7065 nan 0.0100 -0.0001
940 0.7043 nan 0.0100 -0.0001
960 0.7021 nan 0.0100 -0.0000
980 0.7004 nan 0.0100 -0.0001
1000 0.6984 nan 0.0100 -0.0002
1020 0.6965 nan 0.0100 -0.0000
1040 0.6946 nan 0.0100 0.0000
1060 0.6921 nan 0.0100 -0.0000
1080 0.6904 nan 0.0100 0.0000
1100 0.6885 nan 0.0100 -0.0001
- Fold09.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3235 nan 0.0100 0.0038
2 1.3154 nan 0.0100 0.0039
3 1.3076 nan 0.0100 0.0039
4 1.2996 nan 0.0100 0.0041
5 1.2922 nan 0.0100 0.0039
6 1.2850 nan 0.0100 0.0036
7 1.2776 nan 0.0100 0.0036
8 1.2707 nan 0.0100 0.0032
9 1.2639 nan 0.0100 0.0034
10 1.2567 nan 0.0100 0.0033
20 1.1932 nan 0.0100 0.0027
40 1.0950 nan 0.0100 0.0018
60 1.0225 nan 0.0100 0.0014
80 0.9683 nan 0.0100 0.0006
100 0.9283 nan 0.0100 0.0009
120 0.8965 nan 0.0100 0.0005
140 0.8721 nan 0.0100 0.0003
160 0.8520 nan 0.0100 0.0003
180 0.8356 nan 0.0100 0.0001
200 0.8212 nan 0.0100 0.0001
220 0.8099 nan 0.0100 -0.0000
240 0.7983 nan 0.0100 0.0000
260 0.7884 nan 0.0100 0.0000
280 0.7803 nan 0.0100 -0.0000
300 0.7727 nan 0.0100 0.0001
320 0.7645 nan 0.0100 -0.0001
340 0.7577 nan 0.0100 0.0001
360 0.7511 nan 0.0100 0.0000
380 0.7447 nan 0.0100 0.0001
400 0.7390 nan 0.0100 -0.0000
420 0.7344 nan 0.0100 -0.0000
440 0.7296 nan 0.0100 -0.0000
460 0.7249 nan 0.0100 -0.0002
480 0.7200 nan 0.0100 -0.0001
500 0.7155 nan 0.0100 -0.0001
520 0.7113 nan 0.0100 -0.0000
540 0.7073 nan 0.0100 -0.0000
560 0.7030 nan 0.0100 -0.0001
580 0.6991 nan 0.0100 -0.0001
600 0.6950 nan 0.0100 -0.0000
620 0.6907 nan 0.0100 -0.0000
640 0.6874 nan 0.0100 -0.0001
660 0.6838 nan 0.0100 -0.0001
680 0.6808 nan 0.0100 -0.0000
700 0.6777 nan 0.0100 -0.0002
720 0.6745 nan 0.0100 -0.0002
740 0.6712 nan 0.0100 -0.0003
760 0.6684 nan 0.0100 -0.0002
780 0.6653 nan 0.0100 -0.0001
800 0.6620 nan 0.0100 -0.0001
820 0.6590 nan 0.0100 -0.0001
840 0.6560 nan 0.0100 -0.0001
860 0.6531 nan 0.0100 -0.0002
880 0.6504 nan 0.0100 -0.0001
900 0.6477 nan 0.0100 -0.0000
920 0.6456 nan 0.0100 -0.0001
940 0.6430 nan 0.0100 -0.0001
960 0.6404 nan 0.0100 -0.0001
980 0.6378 nan 0.0100 -0.0001
1000 0.6353 nan 0.0100 -0.0001
1020 0.6329 nan 0.0100 -0.0001
1040 0.6307 nan 0.0100 -0.0001
1060 0.6282 nan 0.0100 -0.0001
1080 0.6257 nan 0.0100 -0.0001
1100 0.6224 nan 0.0100 -0.0000
- Fold09.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2726 nan 0.1000 0.0277
2 1.2287 nan 0.1000 0.0237
3 1.1932 nan 0.1000 0.0190
4 1.1577 nan 0.1000 0.0148
5 1.1365 nan 0.1000 0.0078
6 1.1096 nan 0.1000 0.0116
7 1.0892 nan 0.1000 0.0100
8 1.0689 nan 0.1000 0.0075
9 1.0513 nan 0.1000 0.0055
10 1.0355 nan 0.1000 0.0061
20 0.9466 nan 0.1000 0.0020
40 0.8660 nan 0.1000 0.0003
60 0.8307 nan 0.1000 0.0001
80 0.8063 nan 0.1000 -0.0002
100 0.7884 nan 0.1000 -0.0004
120 0.7741 nan 0.1000 -0.0002
140 0.7621 nan 0.1000 -0.0008
160 0.7538 nan 0.1000 -0.0022
180 0.7464 nan 0.1000 0.0000
200 0.7401 nan 0.1000 -0.0005
220 0.7352 nan 0.1000 -0.0008
240 0.7286 nan 0.1000 -0.0007
260 0.7240 nan 0.1000 -0.0009
280 0.7194 nan 0.1000 -0.0003
300 0.7172 nan 0.1000 -0.0004
320 0.7119 nan 0.1000 -0.0008
340 0.7068 nan 0.1000 -0.0006
360 0.7045 nan 0.1000 -0.0009
380 0.7014 nan 0.1000 -0.0012
400 0.6971 nan 0.1000 -0.0015
420 0.6950 nan 0.1000 -0.0006
440 0.6919 nan 0.1000 -0.0008
460 0.6877 nan 0.1000 -0.0007
480 0.6858 nan 0.1000 -0.0011
500 0.6835 nan 0.1000 -0.0004
520 0.6805 nan 0.1000 -0.0013
540 0.6783 nan 0.1000 -0.0010
560 0.6757 nan 0.1000 -0.0013
580 0.6732 nan 0.1000 -0.0013
600 0.6700 nan 0.1000 -0.0006
620 0.6689 nan 0.1000 -0.0001
640 0.6661 nan 0.1000 -0.0010
660 0.6643 nan 0.1000 -0.0015
680 0.6626 nan 0.1000 -0.0005
700 0.6605 nan 0.1000 -0.0013
720 0.6597 nan 0.1000 -0.0014
740 0.6565 nan 0.1000 -0.0006
760 0.6544 nan 0.1000 -0.0003
780 0.6535 nan 0.1000 -0.0002
800 0.6515 nan 0.1000 -0.0009
820 0.6494 nan 0.1000 -0.0010
840 0.6483 nan 0.1000 -0.0019
860 0.6459 nan 0.1000 -0.0005
880 0.6447 nan 0.1000 -0.0002
900 0.6426 nan 0.1000 -0.0014
920 0.6416 nan 0.1000 -0.0008
940 0.6404 nan 0.1000 -0.0006
960 0.6392 nan 0.1000 -0.0007
980 0.6375 nan 0.1000 -0.0004
1000 0.6358 nan 0.1000 -0.0003
1020 0.6337 nan 0.1000 -0.0005
1040 0.6314 nan 0.1000 -0.0008
1060 0.6306 nan 0.1000 -0.0004
1080 0.6291 nan 0.1000 -0.0015
1100 0.6286 nan 0.1000 -0.0008
- Fold09.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2573 nan 0.1000 0.0347
2 1.1988 nan 0.1000 0.0294
3 1.1544 nan 0.1000 0.0210
4 1.1157 nan 0.1000 0.0204
5 1.0803 nan 0.1000 0.0164
6 1.0499 nan 0.1000 0.0151
7 1.0214 nan 0.1000 0.0130
8 0.9988 nan 0.1000 0.0101
9 0.9782 nan 0.1000 0.0097
10 0.9622 nan 0.1000 0.0076
20 0.8576 nan 0.1000 0.0031
40 0.7791 nan 0.1000 0.0005
60 0.7419 nan 0.1000 0.0012
80 0.7149 nan 0.1000 -0.0014
100 0.6929 nan 0.1000 -0.0010
120 0.6734 nan 0.1000 -0.0006
140 0.6568 nan 0.1000 -0.0010
160 0.6436 nan 0.1000 -0.0005
180 0.6285 nan 0.1000 -0.0012
200 0.6182 nan 0.1000 -0.0018
220 0.6063 nan 0.1000 -0.0010
240 0.5973 nan 0.1000 -0.0010
260 0.5890 nan 0.1000 -0.0006
280 0.5803 nan 0.1000 -0.0012
300 0.5720 nan 0.1000 -0.0013
320 0.5623 nan 0.1000 -0.0007
340 0.5537 nan 0.1000 -0.0012
360 0.5461 nan 0.1000 -0.0009
380 0.5361 nan 0.1000 -0.0011
400 0.5269 nan 0.1000 -0.0008
420 0.5215 nan 0.1000 -0.0014
440 0.5139 nan 0.1000 -0.0008
460 0.5082 nan 0.1000 -0.0007
480 0.5021 nan 0.1000 -0.0006
500 0.4945 nan 0.1000 -0.0011
520 0.4890 nan 0.1000 -0.0009
540 0.4843 nan 0.1000 -0.0005
560 0.4801 nan 0.1000 -0.0006
580 0.4744 nan 0.1000 -0.0007
600 0.4703 nan 0.1000 -0.0009
620 0.4649 nan 0.1000 -0.0008
640 0.4601 nan 0.1000 -0.0008
660 0.4542 nan 0.1000 -0.0010
680 0.4494 nan 0.1000 -0.0005
700 0.4437 nan 0.1000 -0.0009
720 0.4385 nan 0.1000 -0.0006
740 0.4346 nan 0.1000 -0.0017
760 0.4301 nan 0.1000 -0.0008
780 0.4268 nan 0.1000 -0.0009
800 0.4231 nan 0.1000 -0.0006
820 0.4188 nan 0.1000 -0.0006
840 0.4157 nan 0.1000 -0.0007
860 0.4119 nan 0.1000 -0.0007
880 0.4078 nan 0.1000 -0.0004
900 0.4031 nan 0.1000 -0.0004
920 0.3992 nan 0.1000 -0.0005
940 0.3964 nan 0.1000 -0.0007
960 0.3939 nan 0.1000 -0.0013
980 0.3902 nan 0.1000 -0.0004
1000 0.3864 nan 0.1000 -0.0008
1020 0.3833 nan 0.1000 -0.0013
1040 0.3818 nan 0.1000 -0.0008
1060 0.3801 nan 0.1000 -0.0004
1080 0.3764 nan 0.1000 -0.0014
1100 0.3734 nan 0.1000 -0.0012
- Fold09.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold09.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
variable 37: CabinCodeT has no variation.
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2582 nan 0.1000 0.0377
2 1.1871 nan 0.1000 0.0349
3 1.1342 nan 0.1000 0.0263
4 1.0865 nan 0.1000 0.0218
5 1.0490 nan 0.1000 0.0170
6 1.0162 nan 0.1000 0.0145
7 0.9866 nan 0.1000 0.0133
8 0.9627 nan 0.1000 0.0103
9 0.9420 nan 0.1000 0.0094
10 0.9277 nan 0.1000 0.0060
20 0.8186 nan 0.1000 0.0037
40 0.7412 nan 0.1000 0.0004
60 0.6952 nan 0.1000 -0.0009
80 0.6561 nan 0.1000 -0.0013
100 0.6263 nan 0.1000 -0.0025
120 0.6032 nan 0.1000 -0.0004
140 0.5846 nan 0.1000 -0.0023
160 0.5650 nan 0.1000 -0.0022
180 0.5530 nan 0.1000 -0.0012
200 0.5368 nan 0.1000 -0.0013
220 0.5265 nan 0.1000 -0.0012
240 0.5130 nan 0.1000 -0.0016
260 0.5007 nan 0.1000 -0.0017
280 0.4849 nan 0.1000 -0.0013
300 0.4747 nan 0.1000 -0.0011
320 0.4643 nan 0.1000 -0.0009
340 0.4542 nan 0.1000 -0.0008
360 0.4458 nan 0.1000 -0.0014
380 0.4365 nan 0.1000 -0.0016
400 0.4279 nan 0.1000 -0.0013
420 0.4197 nan 0.1000 -0.0008
440 0.4110 nan 0.1000 -0.0013
460 0.4040 nan 0.1000 -0.0010
480 0.3980 nan 0.1000 -0.0008
500 0.3893 nan 0.1000 -0.0018
520 0.3810 nan 0.1000 -0.0012
540 0.3732 nan 0.1000 -0.0015
560 0.3681 nan 0.1000 -0.0008
580 0.3600 nan 0.1000 -0.0008
600 0.3558 nan 0.1000 -0.0007
620 0.3495 nan 0.1000 -0.0006
640 0.3452 nan 0.1000 -0.0004
660 0.3411 nan 0.1000 -0.0011
680 0.3377 nan 0.1000 -0.0003
700 0.3326 nan 0.1000 -0.0009
720 0.3264 nan 0.1000 -0.0008
740 0.3206 nan 0.1000 -0.0015
760 0.3166 nan 0.1000 -0.0014
780 0.3096 nan 0.1000 -0.0003
800 0.3058 nan 0.1000 -0.0014
820 0.2998 nan 0.1000 -0.0005
840 0.2946 nan 0.1000 -0.0015
860 0.2903 nan 0.1000 -0.0010
880 0.2865 nan 0.1000 -0.0008
900 0.2824 nan 0.1000 -0.0011
920 0.2780 nan 0.1000 -0.0005
940 0.2747 nan 0.1000 -0.0010
960 0.2705 nan 0.1000 -0.0008
980 0.2663 nan 0.1000 -0.0004
1000 0.2622 nan 0.1000 -0.0007
1020 0.2583 nan 0.1000 -0.0009
1040 0.2544 nan 0.1000 -0.0009
1060 0.2506 nan 0.1000 -0.0014
1080 0.2465 nan 0.1000 -0.0010
1100 0.2424 nan 0.1000 -0.0012
- Fold09.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3266 nan 0.0100 0.0027
2 1.3210 nan 0.0100 0.0027
3 1.3155 nan 0.0100 0.0026
4 1.3107 nan 0.0100 0.0025
5 1.3058 nan 0.0100 0.0025
6 1.3001 nan 0.0100 0.0026
7 1.2952 nan 0.0100 0.0025
8 1.2907 nan 0.0100 0.0025
9 1.2858 nan 0.0100 0.0024
10 1.2813 nan 0.0100 0.0023
20 1.2388 nan 0.0100 0.0019
40 1.1742 nan 0.0100 0.0013
60 1.1278 nan 0.0100 0.0009
80 1.0931 nan 0.0100 0.0007
100 1.0646 nan 0.0100 0.0006
120 1.0408 nan 0.0100 0.0005
140 1.0211 nan 0.0100 0.0003
160 1.0036 nan 0.0100 0.0003
180 0.9890 nan 0.0100 0.0003
200 0.9767 nan 0.0100 0.0001
220 0.9655 nan 0.0100 0.0002
240 0.9559 nan 0.0100 0.0002
260 0.9474 nan 0.0100 0.0001
280 0.9393 nan 0.0100 0.0002
300 0.9316 nan 0.0100 -0.0000
320 0.9249 nan 0.0100 0.0001
340 0.9192 nan 0.0100 0.0001
360 0.9133 nan 0.0100 -0.0000
380 0.9076 nan 0.0100 0.0001
400 0.9026 nan 0.0100 0.0000
420 0.8982 nan 0.0100 -0.0000
440 0.8938 nan 0.0100 0.0000
460 0.8898 nan 0.0100 0.0000
480 0.8857 nan 0.0100 -0.0000
500 0.8819 nan 0.0100 0.0001
520 0.8784 nan 0.0100 0.0000
540 0.8752 nan 0.0100 -0.0000
560 0.8718 nan 0.0100 -0.0000
580 0.8686 nan 0.0100 0.0000
600 0.8657 nan 0.0100 0.0000
620 0.8629 nan 0.0100 0.0000
640 0.8602 nan 0.0100 0.0000
660 0.8576 nan 0.0100 0.0001
680 0.8551 nan 0.0100 0.0000
700 0.8525 nan 0.0100 -0.0000
720 0.8500 nan 0.0100 -0.0000
740 0.8476 nan 0.0100 -0.0000
760 0.8455 nan 0.0100 -0.0000
780 0.8435 nan 0.0100 -0.0000
800 0.8417 nan 0.0100 -0.0001
820 0.8399 nan 0.0100 -0.0001
840 0.8380 nan 0.0100 -0.0000
860 0.8364 nan 0.0100 -0.0000
880 0.8345 nan 0.0100 -0.0000
900 0.8327 nan 0.0100 -0.0001
920 0.8307 nan 0.0100 -0.0001
940 0.8290 nan 0.0100 -0.0001
960 0.8276 nan 0.0100 -0.0001
980 0.8264 nan 0.0100 -0.0000
1000 0.8249 nan 0.0100 -0.0001
1020 0.8236 nan 0.0100 -0.0000
1040 0.8222 nan 0.0100 -0.0000
1060 0.8209 nan 0.0100 -0.0000
1080 0.8194 nan 0.0100 -0.0001
1100 0.8184 nan 0.0100 -0.0000
- Fold10.Rep5: shrinkage=0.01, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3244 nan 0.0100 0.0035
2 1.3169 nan 0.0100 0.0032
3 1.3100 nan 0.0100 0.0031
4 1.3032 nan 0.0100 0.0033
5 1.2966 nan 0.0100 0.0032
6 1.2903 nan 0.0100 0.0032
7 1.2839 nan 0.0100 0.0032
8 1.2782 nan 0.0100 0.0031
9 1.2721 nan 0.0100 0.0029
10 1.2664 nan 0.0100 0.0028
20 1.2141 nan 0.0100 0.0023
40 1.1307 nan 0.0100 0.0017
60 1.0702 nan 0.0100 0.0012
80 1.0249 nan 0.0100 0.0008
100 0.9884 nan 0.0100 0.0007
120 0.9617 nan 0.0100 0.0006
140 0.9396 nan 0.0100 0.0004
160 0.9223 nan 0.0100 0.0003
180 0.9077 nan 0.0100 0.0002
200 0.8963 nan 0.0100 0.0001
220 0.8859 nan 0.0100 -0.0000
240 0.8762 nan 0.0100 0.0001
260 0.8669 nan 0.0100 0.0001
280 0.8589 nan 0.0100 -0.0000
300 0.8518 nan 0.0100 0.0000
320 0.8454 nan 0.0100 0.0001
340 0.8392 nan 0.0100 -0.0000
360 0.8328 nan 0.0100 0.0000
380 0.8271 nan 0.0100 0.0000
400 0.8217 nan 0.0100 -0.0001
420 0.8172 nan 0.0100 -0.0000
440 0.8132 nan 0.0100 0.0000
460 0.8094 nan 0.0100 -0.0000
480 0.8054 nan 0.0100 0.0001
500 0.8015 nan 0.0100 -0.0000
520 0.7976 nan 0.0100 -0.0000
540 0.7941 nan 0.0100 -0.0000
560 0.7905 nan 0.0100 0.0001
580 0.7876 nan 0.0100 -0.0001
600 0.7844 nan 0.0100 -0.0001
620 0.7813 nan 0.0100 -0.0001
640 0.7780 nan 0.0100 -0.0000
660 0.7750 nan 0.0100 0.0000
680 0.7723 nan 0.0100 -0.0001
700 0.7697 nan 0.0100 -0.0000
720 0.7674 nan 0.0100 0.0000
740 0.7648 nan 0.0100 -0.0000
760 0.7621 nan 0.0100 -0.0000
780 0.7591 nan 0.0100 -0.0001
800 0.7572 nan 0.0100 -0.0001
820 0.7549 nan 0.0100 -0.0000
840 0.7524 nan 0.0100 -0.0001
860 0.7500 nan 0.0100 -0.0001
880 0.7477 nan 0.0100 -0.0000
900 0.7453 nan 0.0100 -0.0001
920 0.7428 nan 0.0100 -0.0001
940 0.7411 nan 0.0100 -0.0002
960 0.7391 nan 0.0100 -0.0002
980 0.7371 nan 0.0100 -0.0001
1000 0.7352 nan 0.0100 -0.0001
1020 0.7330 nan 0.0100 -0.0001
1040 0.7310 nan 0.0100 -0.0001
1060 0.7288 nan 0.0100 -0.0001
1080 0.7272 nan 0.0100 -0.0002
1100 0.7254 nan 0.0100 -0.0001
- Fold10.Rep5: shrinkage=0.01, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3243 nan 0.0100 0.0039
2 1.3164 nan 0.0100 0.0039
3 1.3093 nan 0.0100 0.0036
4 1.3018 nan 0.0100 0.0033
5 1.2943 nan 0.0100 0.0035
6 1.2874 nan 0.0100 0.0034
7 1.2799 nan 0.0100 0.0030
8 1.2729 nan 0.0100 0.0033
9 1.2663 nan 0.0100 0.0034
10 1.2599 nan 0.0100 0.0033
20 1.2014 nan 0.0100 0.0024
40 1.1093 nan 0.0100 0.0018
60 1.0414 nan 0.0100 0.0013
80 0.9924 nan 0.0100 0.0010
100 0.9527 nan 0.0100 0.0008
120 0.9240 nan 0.0100 0.0006
140 0.9017 nan 0.0100 0.0004
160 0.8831 nan 0.0100 0.0003
180 0.8676 nan 0.0100 0.0002
200 0.8538 nan 0.0100 0.0001
220 0.8422 nan 0.0100 0.0002
240 0.8320 nan 0.0100 0.0000
260 0.8229 nan 0.0100 0.0001
280 0.8148 nan 0.0100 -0.0001
300 0.8077 nan 0.0100 -0.0001
320 0.8000 nan 0.0100 0.0000
340 0.7939 nan 0.0100 0.0001
360 0.7879 nan 0.0100 -0.0001
380 0.7825 nan 0.0100 -0.0001
400 0.7773 nan 0.0100 -0.0000
420 0.7724 nan 0.0100 -0.0001
440 0.7671 nan 0.0100 -0.0000
460 0.7625 nan 0.0100 -0.0001
480 0.7577 nan 0.0100 -0.0000
500 0.7530 nan 0.0100 -0.0000
520 0.7489 nan 0.0100 -0.0000
540 0.7451 nan 0.0100 -0.0001
560 0.7414 nan 0.0100 -0.0001
580 0.7377 nan 0.0100 -0.0000
600 0.7344 nan 0.0100 -0.0002
620 0.7311 nan 0.0100 -0.0002
640 0.7275 nan 0.0100 0.0000
660 0.7246 nan 0.0100 -0.0002
680 0.7212 nan 0.0100 -0.0001
700 0.7179 nan 0.0100 -0.0000
720 0.7148 nan 0.0100 -0.0001
740 0.7116 nan 0.0100 -0.0001
760 0.7086 nan 0.0100 -0.0001
780 0.7063 nan 0.0100 -0.0001
800 0.7035 nan 0.0100 -0.0002
820 0.7002 nan 0.0100 -0.0002
840 0.6977 nan 0.0100 -0.0002
860 0.6948 nan 0.0100 -0.0001
880 0.6914 nan 0.0100 -0.0001
900 0.6882 nan 0.0100 -0.0001
920 0.6857 nan 0.0100 -0.0002
940 0.6832 nan 0.0100 -0.0000
960 0.6805 nan 0.0100 -0.0000
980 0.6780 nan 0.0100 -0.0001
1000 0.6750 nan 0.0100 -0.0001
1020 0.6723 nan 0.0100 -0.0000
1040 0.6700 nan 0.0100 -0.0001
1060 0.6673 nan 0.0100 -0.0001
1080 0.6651 nan 0.0100 -0.0002
1100 0.6628 nan 0.0100 -0.0001
- Fold10.Rep5: shrinkage=0.01, interaction.depth=3, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2811 nan 0.1000 0.0263
2 1.2358 nan 0.1000 0.0208
3 1.2003 nan 0.1000 0.0167
4 1.1715 nan 0.1000 0.0136
5 1.1459 nan 0.1000 0.0130
6 1.1271 nan 0.1000 0.0082
7 1.1069 nan 0.1000 0.0082
8 1.0900 nan 0.1000 0.0083
9 1.0747 nan 0.1000 0.0060
10 1.0600 nan 0.1000 0.0064
20 0.9761 nan 0.1000 0.0020
40 0.9034 nan 0.1000 -0.0002
60 0.8692 nan 0.1000 -0.0005
80 0.8464 nan 0.1000 -0.0013
100 0.8278 nan 0.1000 -0.0006
120 0.8148 nan 0.1000 -0.0003
140 0.8047 nan 0.1000 -0.0004
160 0.7953 nan 0.1000 -0.0009
180 0.7897 nan 0.1000 -0.0003
200 0.7817 nan 0.1000 -0.0003
220 0.7744 nan 0.1000 -0.0009
240 0.7697 nan 0.1000 -0.0006
260 0.7660 nan 0.1000 -0.0013
280 0.7605 nan 0.1000 -0.0004
300 0.7568 nan 0.1000 -0.0006
320 0.7541 nan 0.1000 -0.0011
340 0.7505 nan 0.1000 -0.0005
360 0.7468 nan 0.1000 -0.0008
380 0.7430 nan 0.1000 -0.0001
400 0.7403 nan 0.1000 -0.0006
420 0.7357 nan 0.1000 -0.0006
440 0.7326 nan 0.1000 -0.0012
460 0.7306 nan 0.1000 -0.0004
480 0.7281 nan 0.1000 -0.0013
500 0.7261 nan 0.1000 -0.0003
520 0.7231 nan 0.1000 -0.0005
540 0.7212 nan 0.1000 -0.0005
560 0.7192 nan 0.1000 -0.0010
580 0.7180 nan 0.1000 -0.0003
600 0.7154 nan 0.1000 -0.0006
620 0.7141 nan 0.1000 -0.0011
640 0.7116 nan 0.1000 -0.0005
660 0.7099 nan 0.1000 -0.0006
680 0.7080 nan 0.1000 -0.0011
700 0.7080 nan 0.1000 -0.0005
720 0.7054 nan 0.1000 -0.0014
740 0.7033 nan 0.1000 -0.0007
760 0.7012 nan 0.1000 -0.0005
780 0.6988 nan 0.1000 -0.0006
800 0.6969 nan 0.1000 -0.0007
820 0.6961 nan 0.1000 -0.0006
840 0.6945 nan 0.1000 -0.0005
860 0.6925 nan 0.1000 -0.0011
880 0.6904 nan 0.1000 -0.0012
900 0.6892 nan 0.1000 -0.0008
920 0.6874 nan 0.1000 -0.0013
940 0.6858 nan 0.1000 -0.0011
960 0.6837 nan 0.1000 -0.0006
980 0.6826 nan 0.1000 -0.0011
1000 0.6822 nan 0.1000 -0.0004
1020 0.6809 nan 0.1000 -0.0005
1040 0.6799 nan 0.1000 -0.0010
1060 0.6785 nan 0.1000 -0.0007
1080 0.6766 nan 0.1000 -0.0003
1100 0.6751 nan 0.1000 -0.0019
- Fold10.Rep5: shrinkage=0.10, interaction.depth=1, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2664 nan 0.1000 0.0321
2 1.2099 nan 0.1000 0.0256
3 1.1637 nan 0.1000 0.0211
4 1.1230 nan 0.1000 0.0198
5 1.0900 nan 0.1000 0.0157
6 1.0608 nan 0.1000 0.0119
7 1.0382 nan 0.1000 0.0110
8 1.0174 nan 0.1000 0.0096
9 0.9990 nan 0.1000 0.0088
10 0.9820 nan 0.1000 0.0071
20 0.8933 nan 0.1000 -0.0009
40 0.8250 nan 0.1000 0.0001
60 0.7875 nan 0.1000 -0.0006
80 0.7642 nan 0.1000 -0.0014
100 0.7411 nan 0.1000 -0.0009
120 0.7274 nan 0.1000 -0.0008
140 0.7110 nan 0.1000 -0.0012
160 0.7003 nan 0.1000 -0.0014
180 0.6818 nan 0.1000 -0.0012
200 0.6682 nan 0.1000 0.0003
220 0.6575 nan 0.1000 -0.0017
240 0.6457 nan 0.1000 -0.0009
260 0.6326 nan 0.1000 -0.0010
280 0.6202 nan 0.1000 -0.0015
300 0.6097 nan 0.1000 -0.0007
320 0.6024 nan 0.1000 -0.0010
340 0.5946 nan 0.1000 -0.0009
360 0.5889 nan 0.1000 -0.0008
380 0.5809 nan 0.1000 -0.0011
400 0.5732 nan 0.1000 -0.0009
420 0.5656 nan 0.1000 -0.0017
440 0.5589 nan 0.1000 -0.0005
460 0.5530 nan 0.1000 -0.0007
480 0.5461 nan 0.1000 -0.0008
500 0.5404 nan 0.1000 -0.0008
520 0.5343 nan 0.1000 -0.0007
540 0.5284 nan 0.1000 -0.0009
560 0.5236 nan 0.1000 -0.0010
580 0.5204 nan 0.1000 -0.0017
600 0.5158 nan 0.1000 -0.0008
620 0.5116 nan 0.1000 -0.0011
640 0.5062 nan 0.1000 -0.0008
660 0.5019 nan 0.1000 -0.0002
680 0.4978 nan 0.1000 -0.0005
700 0.4930 nan 0.1000 -0.0009
720 0.4883 nan 0.1000 -0.0017
740 0.4839 nan 0.1000 -0.0009
760 0.4785 nan 0.1000 -0.0008
780 0.4742 nan 0.1000 -0.0010
800 0.4691 nan 0.1000 -0.0005
820 0.4637 nan 0.1000 -0.0005
840 0.4601 nan 0.1000 -0.0002
860 0.4561 nan 0.1000 -0.0015
880 0.4521 nan 0.1000 -0.0009
900 0.4482 nan 0.1000 -0.0006
920 0.4444 nan 0.1000 -0.0009
940 0.4414 nan 0.1000 -0.0007
960 0.4367 nan 0.1000 -0.0008
980 0.4344 nan 0.1000 -0.0014
1000 0.4324 nan 0.1000 -0.0011
1020 0.4293 nan 0.1000 -0.0007
1040 0.4257 nan 0.1000 -0.0005
1060 0.4225 nan 0.1000 -0.0003
1080 0.4188 nan 0.1000 -0.0008
1100 0.4149 nan 0.1000 -0.0010
- Fold10.Rep5: shrinkage=0.10, interaction.depth=2, n.minobsinnode=10, n.trees=1100
+ Fold10.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.2549 nan 0.1000 0.0340
2 1.1983 nan 0.1000 0.0267
3 1.1467 nan 0.1000 0.0215
4 1.1076 nan 0.1000 0.0183
5 1.0669 nan 0.1000 0.0165
6 1.0356 nan 0.1000 0.0139
7 1.0078 nan 0.1000 0.0133
8 0.9895 nan 0.1000 0.0069
9 0.9700 nan 0.1000 0.0086
10 0.9533 nan 0.1000 0.0089
20 0.8542 nan 0.1000 0.0018
40 0.7820 nan 0.1000 -0.0014
60 0.7416 nan 0.1000 -0.0006
80 0.7069 nan 0.1000 -0.0011
100 0.6797 nan 0.1000 -0.0010
120 0.6615 nan 0.1000 -0.0015
140 0.6405 nan 0.1000 -0.0008
160 0.6249 nan 0.1000 -0.0017
180 0.6052 nan 0.1000 -0.0011
200 0.5907 nan 0.1000 -0.0013
220 0.5740 nan 0.1000 -0.0006
240 0.5568 nan 0.1000 -0.0004
260 0.5409 nan 0.1000 -0.0003
280 0.5299 nan 0.1000 -0.0012
300 0.5188 nan 0.1000 -0.0012
320 0.5065 nan 0.1000 -0.0011
340 0.4955 nan 0.1000 -0.0005
360 0.4852 nan 0.1000 -0.0005
380 0.4747 nan 0.1000 -0.0010
400 0.4663 nan 0.1000 -0.0009
420 0.4569 nan 0.1000 -0.0010
440 0.4489 nan 0.1000 -0.0007
460 0.4399 nan 0.1000 -0.0008
480 0.4323 nan 0.1000 -0.0007
500 0.4245 nan 0.1000 -0.0010
520 0.4194 nan 0.1000 -0.0013
540 0.4141 nan 0.1000 -0.0015
560 0.4085 nan 0.1000 -0.0015
580 0.4029 nan 0.1000 -0.0012
600 0.3963 nan 0.1000 -0.0013
620 0.3907 nan 0.1000 -0.0011
640 0.3868 nan 0.1000 -0.0021
660 0.3808 nan 0.1000 -0.0009
680 0.3755 nan 0.1000 -0.0003
700 0.3709 nan 0.1000 -0.0012
720 0.3648 nan 0.1000 -0.0004
740 0.3599 nan 0.1000 -0.0012
760 0.3538 nan 0.1000 -0.0009
780 0.3497 nan 0.1000 -0.0014
800 0.3439 nan 0.1000 -0.0012
820 0.3394 nan 0.1000 -0.0007
840 0.3358 nan 0.1000 -0.0009
860 0.3316 nan 0.1000 -0.0005
880 0.3282 nan 0.1000 -0.0009
900 0.3241 nan 0.1000 -0.0007
920 0.3207 nan 0.1000 -0.0013
940 0.3182 nan 0.1000 -0.0007
960 0.3142 nan 0.1000 -0.0012
980 0.3093 nan 0.1000 -0.0008
1000 0.3058 nan 0.1000 -0.0008
1020 0.3016 nan 0.1000 -0.0010
1040 0.2977 nan 0.1000 -0.0006
1060 0.2940 nan 0.1000 -0.0009
1080 0.2892 nan 0.1000 -0.0010
1100 0.2855 nan 0.1000 -0.0007
- Fold10.Rep5: shrinkage=0.10, interaction.depth=3, n.minobsinnode=10, n.trees=1100
Aggregating results
Selecting tuning parameters
Fitting n.trees = 1100, interaction.depth = 3, shrinkage = 0.01, n.minobsinnode = 10 on full training set
Iter TrainDeviance ValidDeviance StepSize Improve
1 1.3241 nan 0.0100 0.0040
2 1.3155 nan 0.0100 0.0039
3 1.3076 nan 0.0100 0.0041
4 1.3003 nan 0.0100 0.0034
5 1.2924 nan 0.0100 0.0038
6 1.2854 nan 0.0100 0.0034
7 1.2780 nan 0.0100 0.0032
8 1.2707 nan 0.0100 0.0035
9 1.2634 nan 0.0100 0.0034
10 1.2569 nan 0.0100 0.0033
20 1.1942 nan 0.0100 0.0029
40 1.0986 nan 0.0100 0.0019
60 1.0287 nan 0.0100 0.0015
80 0.9768 nan 0.0100 0.0010
100 0.9373 nan 0.0100 0.0007
120 0.9069 nan 0.0100 0.0002
140 0.8838 nan 0.0100 0.0004
160 0.8639 nan 0.0100 0.0004
180 0.8472 nan 0.0100 0.0001
200 0.8327 nan 0.0100 0.0002
220 0.8204 nan 0.0100 0.0001
240 0.8095 nan 0.0100 0.0001
260 0.7999 nan 0.0100 0.0001
280 0.7912 nan 0.0100 0.0001
300 0.7826 nan 0.0100 0.0001
320 0.7756 nan 0.0100 -0.0000
340 0.7691 nan 0.0100 0.0000
360 0.7629 nan 0.0100 0.0000
380 0.7569 nan 0.0100 0.0000
400 0.7518 nan 0.0100 -0.0001
420 0.7466 nan 0.0100 -0.0001
440 0.7410 nan 0.0100 -0.0001
460 0.7365 nan 0.0100 0.0000
480 0.7322 nan 0.0100 0.0000
500 0.7280 nan 0.0100 -0.0001
520 0.7235 nan 0.0100 -0.0000
540 0.7197 nan 0.0100 0.0000
560 0.7158 nan 0.0100 -0.0000
580 0.7121 nan 0.0100 -0.0001
600 0.7079 nan 0.0100 -0.0000
620 0.7043 nan 0.0100 -0.0001
640 0.7006 nan 0.0100 -0.0001
660 0.6973 nan 0.0100 -0.0001
680 0.6939 nan 0.0100 -0.0001
700 0.6910 nan 0.0100 -0.0001
720 0.6883 nan 0.0100 -0.0000
740 0.6850 nan 0.0100 -0.0000
760 0.6826 nan 0.0100 -0.0000
780 0.6799 nan 0.0100 -0.0001
800 0.6773 nan 0.0100 -0.0001
820 0.6743 nan 0.0100 -0.0000
840 0.6715 nan 0.0100 -0.0001
860 0.6689 nan 0.0100 -0.0001
880 0.6660 nan 0.0100 -0.0002
900 0.6631 nan 0.0100 -0.0001
920 0.6606 nan 0.0100 -0.0001
940 0.6585 nan 0.0100 -0.0001
960 0.6560 nan 0.0100 -0.0001
980 0.6532 nan 0.0100 -0.0001
1000 0.6506 nan 0.0100 -0.0001
1020 0.6483 nan 0.0100 -0.0002
1040 0.6456 nan 0.0100 -0.0002
1060 0.6433 nan 0.0100 -0.0001
1080 0.6407 nan 0.0100 -0.0001
1100 0.6391 nan 0.0100 -0.0001
boostFitStochastic Gradient Boosting
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Resampling results across tuning parameters:
shrinkage interaction.depth n.trees ROC Sens Spec
0.01 1 500 0.8687740 0.7176975 0.8812727
0.01 1 700 0.8715465 0.7310756 0.8827340
0.01 1 900 0.8731344 0.7456975 0.8798182
0.01 1 1100 0.8737417 0.7614454 0.8790909
0.01 2 500 0.8768379 0.7410252 0.8819933
0.01 2 700 0.8781806 0.7549916 0.8808956
0.01 2 900 0.8791223 0.7614118 0.8801751
0.01 2 1100 0.8801043 0.7613950 0.8809024
0.01 3 500 0.8790775 0.7526218 0.8823636
0.01 3 700 0.8808075 0.7584874 0.8812727
0.01 3 900 0.8815746 0.7549748 0.8809091
0.01 3 1100 0.8817104 0.7532269 0.8849091
0.10 1 500 0.8699484 0.7480168 0.8721751
0.10 1 700 0.8708636 0.7485882 0.8736162
0.10 1 900 0.8686151 0.7422017 0.8743367
0.10 1 1100 0.8689881 0.7428067 0.8725051
0.10 2 500 0.8801393 0.7473950 0.8837845
0.10 2 700 0.8799571 0.7467563 0.8819798
0.10 2 900 0.8790711 0.7438992 0.8790370
0.10 2 1100 0.8773468 0.7474118 0.8757778
0.10 3 500 0.8803987 0.7450252 0.8790774
0.10 3 700 0.8805280 0.7438655 0.8746869
0.10 3 900 0.8793306 0.7438655 0.8692189
0.10 3 1100 0.8785908 0.7432773 0.8713872
Tuning parameter 'n.minobsinnode' was held constant at a value of 10
ROC was used to select the optimal model using the largest value.
The final values used for the model were n.trees = 1100, interaction.depth = 3, shrinkage
= 0.01 and n.minobsinnode = 10.
plot(boostFit)xyplot(oobag.improve~1:1100,data=boostFit$finalModel,alpha=.5,xlab = 'n.trees')plot(varImp(boostFit))adapt_ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
verboseIter = T,
classProbs = TRUE,
summaryFunction = twoClassSummary,
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
search = 'random'
)
rfGrid <- expand.grid(mtry=c(5,10,15,20,25))
set.seed(1)
rfFit.y <- train(
form = Survived~.,
data = train.imp,
method = 'rf',
trControl = adapt_ctrl,
# metric = "Kappa",
tuneGrid = rfGrid,
verbose = TRUE,
ntree = 400
)The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: mtry= 5
- Fold01.Rep1: mtry= 5
+ Fold01.Rep1: mtry=10
- Fold01.Rep1: mtry=10
+ Fold01.Rep1: mtry=15
- Fold01.Rep1: mtry=15
+ Fold01.Rep1: mtry=20
- Fold01.Rep1: mtry=20
+ Fold01.Rep1: mtry=25
- Fold01.Rep1: mtry=25
+ Fold02.Rep1: mtry= 5
- Fold02.Rep1: mtry= 5
+ Fold02.Rep1: mtry=10
- Fold02.Rep1: mtry=10
+ Fold02.Rep1: mtry=15
- Fold02.Rep1: mtry=15
+ Fold02.Rep1: mtry=20
- Fold02.Rep1: mtry=20
+ Fold02.Rep1: mtry=25
- Fold02.Rep1: mtry=25
+ Fold03.Rep1: mtry= 5
- Fold03.Rep1: mtry= 5
+ Fold03.Rep1: mtry=10
- Fold03.Rep1: mtry=10
+ Fold03.Rep1: mtry=15
- Fold03.Rep1: mtry=15
+ Fold03.Rep1: mtry=20
- Fold03.Rep1: mtry=20
+ Fold03.Rep1: mtry=25
- Fold03.Rep1: mtry=25
+ Fold04.Rep1: mtry= 5
- Fold04.Rep1: mtry= 5
+ Fold04.Rep1: mtry=10
- Fold04.Rep1: mtry=10
+ Fold04.Rep1: mtry=15
- Fold04.Rep1: mtry=15
+ Fold04.Rep1: mtry=20
- Fold04.Rep1: mtry=20
+ Fold04.Rep1: mtry=25
- Fold04.Rep1: mtry=25
+ Fold05.Rep1: mtry= 5
- Fold05.Rep1: mtry= 5
+ Fold05.Rep1: mtry=10
- Fold05.Rep1: mtry=10
+ Fold05.Rep1: mtry=15
- Fold05.Rep1: mtry=15
+ Fold05.Rep1: mtry=20
- Fold05.Rep1: mtry=20
+ Fold05.Rep1: mtry=25
- Fold05.Rep1: mtry=25
+ Fold06.Rep1: mtry= 5
- Fold06.Rep1: mtry= 5
+ Fold06.Rep1: mtry=10
- Fold06.Rep1: mtry=10
+ Fold06.Rep1: mtry=15
- Fold06.Rep1: mtry=15
+ Fold06.Rep1: mtry=20
- Fold06.Rep1: mtry=20
+ Fold06.Rep1: mtry=25
- Fold06.Rep1: mtry=25
+ Fold07.Rep1: mtry= 5
- Fold07.Rep1: mtry= 5
+ Fold07.Rep1: mtry=10
- Fold07.Rep1: mtry=10
+ Fold07.Rep1: mtry=15
- Fold07.Rep1: mtry=15
+ Fold07.Rep1: mtry=20
- Fold07.Rep1: mtry=20
+ Fold07.Rep1: mtry=25
- Fold07.Rep1: mtry=25
+ Fold08.Rep1: mtry= 5
- Fold08.Rep1: mtry= 5
+ Fold08.Rep1: mtry=10
- Fold08.Rep1: mtry=10
+ Fold08.Rep1: mtry=15
- Fold08.Rep1: mtry=15
+ Fold08.Rep1: mtry=20
- Fold08.Rep1: mtry=20
+ Fold08.Rep1: mtry=25
- Fold08.Rep1: mtry=25
+ Fold09.Rep1: mtry= 5
- Fold09.Rep1: mtry= 5
+ Fold09.Rep1: mtry=10
- Fold09.Rep1: mtry=10
+ Fold09.Rep1: mtry=15
- Fold09.Rep1: mtry=15
+ Fold09.Rep1: mtry=20
- Fold09.Rep1: mtry=20
+ Fold09.Rep1: mtry=25
- Fold09.Rep1: mtry=25
+ Fold10.Rep1: mtry= 5
- Fold10.Rep1: mtry= 5
+ Fold10.Rep1: mtry=10
- Fold10.Rep1: mtry=10
+ Fold10.Rep1: mtry=15
- Fold10.Rep1: mtry=15
+ Fold10.Rep1: mtry=20
- Fold10.Rep1: mtry=20
+ Fold10.Rep1: mtry=25
- Fold10.Rep1: mtry=25
+ Fold01.Rep2: mtry= 5
- Fold01.Rep2: mtry= 5
+ Fold01.Rep2: mtry=10
- Fold01.Rep2: mtry=10
+ Fold01.Rep2: mtry=15
- Fold01.Rep2: mtry=15
+ Fold01.Rep2: mtry=20
- Fold01.Rep2: mtry=20
+ Fold01.Rep2: mtry=25
- Fold01.Rep2: mtry=25
+ Fold02.Rep2: mtry= 5
- Fold02.Rep2: mtry= 5
+ Fold02.Rep2: mtry=10
- Fold02.Rep2: mtry=10
+ Fold02.Rep2: mtry=15
- Fold02.Rep2: mtry=15
+ Fold02.Rep2: mtry=20
- Fold02.Rep2: mtry=20
+ Fold02.Rep2: mtry=25
- Fold02.Rep2: mtry=25
+ Fold03.Rep2: mtry= 5
- Fold03.Rep2: mtry= 5
+ Fold03.Rep2: mtry=10
- Fold03.Rep2: mtry=10
+ Fold03.Rep2: mtry=15
- Fold03.Rep2: mtry=15
+ Fold03.Rep2: mtry=20
- Fold03.Rep2: mtry=20
+ Fold03.Rep2: mtry=25
- Fold03.Rep2: mtry=25
+ Fold04.Rep2: mtry= 5
- Fold04.Rep2: mtry= 5
+ Fold04.Rep2: mtry=10
- Fold04.Rep2: mtry=10
+ Fold04.Rep2: mtry=15
- Fold04.Rep2: mtry=15
+ Fold04.Rep2: mtry=20
- Fold04.Rep2: mtry=20
+ Fold04.Rep2: mtry=25
- Fold04.Rep2: mtry=25
+ Fold05.Rep2: mtry= 5
- Fold05.Rep2: mtry= 5
+ Fold05.Rep2: mtry=10
- Fold05.Rep2: mtry=10
+ Fold05.Rep2: mtry=15
- Fold05.Rep2: mtry=15
+ Fold05.Rep2: mtry=20
- Fold05.Rep2: mtry=20
+ Fold05.Rep2: mtry=25
- Fold05.Rep2: mtry=25
+ Fold06.Rep2: mtry= 5
- Fold06.Rep2: mtry= 5
+ Fold06.Rep2: mtry=10
- Fold06.Rep2: mtry=10
+ Fold06.Rep2: mtry=15
- Fold06.Rep2: mtry=15
+ Fold06.Rep2: mtry=20
- Fold06.Rep2: mtry=20
+ Fold06.Rep2: mtry=25
- Fold06.Rep2: mtry=25
+ Fold07.Rep2: mtry= 5
- Fold07.Rep2: mtry= 5
+ Fold07.Rep2: mtry=10
- Fold07.Rep2: mtry=10
+ Fold07.Rep2: mtry=15
- Fold07.Rep2: mtry=15
+ Fold07.Rep2: mtry=20
- Fold07.Rep2: mtry=20
+ Fold07.Rep2: mtry=25
- Fold07.Rep2: mtry=25
+ Fold08.Rep2: mtry= 5
- Fold08.Rep2: mtry= 5
+ Fold08.Rep2: mtry=10
- Fold08.Rep2: mtry=10
+ Fold08.Rep2: mtry=15
- Fold08.Rep2: mtry=15
+ Fold08.Rep2: mtry=20
- Fold08.Rep2: mtry=20
+ Fold08.Rep2: mtry=25
- Fold08.Rep2: mtry=25
+ Fold09.Rep2: mtry= 5
- Fold09.Rep2: mtry= 5
+ Fold09.Rep2: mtry=10
- Fold09.Rep2: mtry=10
+ Fold09.Rep2: mtry=15
- Fold09.Rep2: mtry=15
+ Fold09.Rep2: mtry=20
- Fold09.Rep2: mtry=20
+ Fold09.Rep2: mtry=25
- Fold09.Rep2: mtry=25
+ Fold10.Rep2: mtry= 5
- Fold10.Rep2: mtry= 5
+ Fold10.Rep2: mtry=10
- Fold10.Rep2: mtry=10
+ Fold10.Rep2: mtry=15
- Fold10.Rep2: mtry=15
+ Fold10.Rep2: mtry=20
- Fold10.Rep2: mtry=20
+ Fold10.Rep2: mtry=25
- Fold10.Rep2: mtry=25
+ Fold01.Rep3: mtry= 5
- Fold01.Rep3: mtry= 5
+ Fold01.Rep3: mtry=10
- Fold01.Rep3: mtry=10
+ Fold01.Rep3: mtry=15
- Fold01.Rep3: mtry=15
+ Fold01.Rep3: mtry=20
- Fold01.Rep3: mtry=20
+ Fold01.Rep3: mtry=25
- Fold01.Rep3: mtry=25
+ Fold02.Rep3: mtry= 5
- Fold02.Rep3: mtry= 5
+ Fold02.Rep3: mtry=10
- Fold02.Rep3: mtry=10
+ Fold02.Rep3: mtry=15
- Fold02.Rep3: mtry=15
+ Fold02.Rep3: mtry=20
- Fold02.Rep3: mtry=20
+ Fold02.Rep3: mtry=25
- Fold02.Rep3: mtry=25
+ Fold03.Rep3: mtry= 5
- Fold03.Rep3: mtry= 5
+ Fold03.Rep3: mtry=10
- Fold03.Rep3: mtry=10
+ Fold03.Rep3: mtry=15
- Fold03.Rep3: mtry=15
+ Fold03.Rep3: mtry=20
- Fold03.Rep3: mtry=20
+ Fold03.Rep3: mtry=25
- Fold03.Rep3: mtry=25
+ Fold04.Rep3: mtry= 5
- Fold04.Rep3: mtry= 5
+ Fold04.Rep3: mtry=10
- Fold04.Rep3: mtry=10
+ Fold04.Rep3: mtry=15
- Fold04.Rep3: mtry=15
+ Fold04.Rep3: mtry=20
- Fold04.Rep3: mtry=20
+ Fold04.Rep3: mtry=25
- Fold04.Rep3: mtry=25
+ Fold05.Rep3: mtry= 5
- Fold05.Rep3: mtry= 5
+ Fold05.Rep3: mtry=10
- Fold05.Rep3: mtry=10
+ Fold05.Rep3: mtry=15
- Fold05.Rep3: mtry=15
+ Fold05.Rep3: mtry=20
- Fold05.Rep3: mtry=20
+ Fold05.Rep3: mtry=25
- Fold05.Rep3: mtry=25
+ Fold06.Rep3: mtry= 5
- Fold06.Rep3: mtry= 5
+ Fold06.Rep3: mtry=10
- Fold06.Rep3: mtry=10
+ Fold06.Rep3: mtry=15
- Fold06.Rep3: mtry=15
+ Fold06.Rep3: mtry=20
- Fold06.Rep3: mtry=20
+ Fold06.Rep3: mtry=25
- Fold06.Rep3: mtry=25
+ Fold07.Rep3: mtry= 5
- Fold07.Rep3: mtry= 5
+ Fold07.Rep3: mtry=10
- Fold07.Rep3: mtry=10
+ Fold07.Rep3: mtry=15
- Fold07.Rep3: mtry=15
+ Fold07.Rep3: mtry=20
- Fold07.Rep3: mtry=20
+ Fold07.Rep3: mtry=25
- Fold07.Rep3: mtry=25
+ Fold08.Rep3: mtry= 5
- Fold08.Rep3: mtry= 5
+ Fold08.Rep3: mtry=10
- Fold08.Rep3: mtry=10
+ Fold08.Rep3: mtry=15
- Fold08.Rep3: mtry=15
+ Fold08.Rep3: mtry=20
- Fold08.Rep3: mtry=20
+ Fold08.Rep3: mtry=25
- Fold08.Rep3: mtry=25
+ Fold09.Rep3: mtry= 5
- Fold09.Rep3: mtry= 5
+ Fold09.Rep3: mtry=10
- Fold09.Rep3: mtry=10
+ Fold09.Rep3: mtry=15
- Fold09.Rep3: mtry=15
+ Fold09.Rep3: mtry=20
- Fold09.Rep3: mtry=20
+ Fold09.Rep3: mtry=25
- Fold09.Rep3: mtry=25
+ Fold10.Rep3: mtry= 5
- Fold10.Rep3: mtry= 5
+ Fold10.Rep3: mtry=10
- Fold10.Rep3: mtry=10
+ Fold10.Rep3: mtry=15
- Fold10.Rep3: mtry=15
+ Fold10.Rep3: mtry=20
- Fold10.Rep3: mtry=20
+ Fold10.Rep3: mtry=25
- Fold10.Rep3: mtry=25
+ Fold01.Rep4: mtry= 5
- Fold01.Rep4: mtry= 5
+ Fold01.Rep4: mtry=10
- Fold01.Rep4: mtry=10
+ Fold01.Rep4: mtry=15
- Fold01.Rep4: mtry=15
+ Fold01.Rep4: mtry=20
- Fold01.Rep4: mtry=20
+ Fold01.Rep4: mtry=25
- Fold01.Rep4: mtry=25
+ Fold02.Rep4: mtry= 5
- Fold02.Rep4: mtry= 5
+ Fold02.Rep4: mtry=10
- Fold02.Rep4: mtry=10
+ Fold02.Rep4: mtry=15
- Fold02.Rep4: mtry=15
+ Fold02.Rep4: mtry=20
- Fold02.Rep4: mtry=20
+ Fold02.Rep4: mtry=25
- Fold02.Rep4: mtry=25
+ Fold03.Rep4: mtry= 5
- Fold03.Rep4: mtry= 5
+ Fold03.Rep4: mtry=10
- Fold03.Rep4: mtry=10
+ Fold03.Rep4: mtry=15
- Fold03.Rep4: mtry=15
+ Fold03.Rep4: mtry=20
- Fold03.Rep4: mtry=20
+ Fold03.Rep4: mtry=25
- Fold03.Rep4: mtry=25
+ Fold04.Rep4: mtry= 5
- Fold04.Rep4: mtry= 5
+ Fold04.Rep4: mtry=10
- Fold04.Rep4: mtry=10
+ Fold04.Rep4: mtry=15
- Fold04.Rep4: mtry=15
+ Fold04.Rep4: mtry=20
- Fold04.Rep4: mtry=20
+ Fold04.Rep4: mtry=25
- Fold04.Rep4: mtry=25
+ Fold05.Rep4: mtry= 5
- Fold05.Rep4: mtry= 5
+ Fold05.Rep4: mtry=10
- Fold05.Rep4: mtry=10
+ Fold05.Rep4: mtry=15
- Fold05.Rep4: mtry=15
+ Fold05.Rep4: mtry=20
- Fold05.Rep4: mtry=20
+ Fold05.Rep4: mtry=25
- Fold05.Rep4: mtry=25
+ Fold06.Rep4: mtry= 5
- Fold06.Rep4: mtry= 5
+ Fold06.Rep4: mtry=10
- Fold06.Rep4: mtry=10
+ Fold06.Rep4: mtry=15
- Fold06.Rep4: mtry=15
+ Fold06.Rep4: mtry=20
- Fold06.Rep4: mtry=20
+ Fold06.Rep4: mtry=25
- Fold06.Rep4: mtry=25
+ Fold07.Rep4: mtry= 5
- Fold07.Rep4: mtry= 5
+ Fold07.Rep4: mtry=10
- Fold07.Rep4: mtry=10
+ Fold07.Rep4: mtry=15
- Fold07.Rep4: mtry=15
+ Fold07.Rep4: mtry=20
- Fold07.Rep4: mtry=20
+ Fold07.Rep4: mtry=25
- Fold07.Rep4: mtry=25
+ Fold08.Rep4: mtry= 5
- Fold08.Rep4: mtry= 5
+ Fold08.Rep4: mtry=10
- Fold08.Rep4: mtry=10
+ Fold08.Rep4: mtry=15
- Fold08.Rep4: mtry=15
+ Fold08.Rep4: mtry=20
- Fold08.Rep4: mtry=20
+ Fold08.Rep4: mtry=25
- Fold08.Rep4: mtry=25
+ Fold09.Rep4: mtry= 5
- Fold09.Rep4: mtry= 5
+ Fold09.Rep4: mtry=10
- Fold09.Rep4: mtry=10
+ Fold09.Rep4: mtry=15
- Fold09.Rep4: mtry=15
+ Fold09.Rep4: mtry=20
- Fold09.Rep4: mtry=20
+ Fold09.Rep4: mtry=25
- Fold09.Rep4: mtry=25
+ Fold10.Rep4: mtry= 5
- Fold10.Rep4: mtry= 5
+ Fold10.Rep4: mtry=10
- Fold10.Rep4: mtry=10
+ Fold10.Rep4: mtry=15
- Fold10.Rep4: mtry=15
+ Fold10.Rep4: mtry=20
- Fold10.Rep4: mtry=20
+ Fold10.Rep4: mtry=25
- Fold10.Rep4: mtry=25
+ Fold01.Rep5: mtry= 5
- Fold01.Rep5: mtry= 5
+ Fold01.Rep5: mtry=10
- Fold01.Rep5: mtry=10
+ Fold01.Rep5: mtry=15
- Fold01.Rep5: mtry=15
+ Fold01.Rep5: mtry=20
- Fold01.Rep5: mtry=20
+ Fold01.Rep5: mtry=25
- Fold01.Rep5: mtry=25
+ Fold02.Rep5: mtry= 5
- Fold02.Rep5: mtry= 5
+ Fold02.Rep5: mtry=10
- Fold02.Rep5: mtry=10
+ Fold02.Rep5: mtry=15
- Fold02.Rep5: mtry=15
+ Fold02.Rep5: mtry=20
- Fold02.Rep5: mtry=20
+ Fold02.Rep5: mtry=25
- Fold02.Rep5: mtry=25
+ Fold03.Rep5: mtry= 5
- Fold03.Rep5: mtry= 5
+ Fold03.Rep5: mtry=10
- Fold03.Rep5: mtry=10
+ Fold03.Rep5: mtry=15
- Fold03.Rep5: mtry=15
+ Fold03.Rep5: mtry=20
- Fold03.Rep5: mtry=20
+ Fold03.Rep5: mtry=25
- Fold03.Rep5: mtry=25
+ Fold04.Rep5: mtry= 5
- Fold04.Rep5: mtry= 5
+ Fold04.Rep5: mtry=10
- Fold04.Rep5: mtry=10
+ Fold04.Rep5: mtry=15
- Fold04.Rep5: mtry=15
+ Fold04.Rep5: mtry=20
- Fold04.Rep5: mtry=20
+ Fold04.Rep5: mtry=25
- Fold04.Rep5: mtry=25
+ Fold05.Rep5: mtry= 5
- Fold05.Rep5: mtry= 5
+ Fold05.Rep5: mtry=10
- Fold05.Rep5: mtry=10
+ Fold05.Rep5: mtry=15
- Fold05.Rep5: mtry=15
+ Fold05.Rep5: mtry=20
- Fold05.Rep5: mtry=20
+ Fold05.Rep5: mtry=25
- Fold05.Rep5: mtry=25
+ Fold06.Rep5: mtry= 5
- Fold06.Rep5: mtry= 5
+ Fold06.Rep5: mtry=10
- Fold06.Rep5: mtry=10
+ Fold06.Rep5: mtry=15
- Fold06.Rep5: mtry=15
+ Fold06.Rep5: mtry=20
- Fold06.Rep5: mtry=20
+ Fold06.Rep5: mtry=25
- Fold06.Rep5: mtry=25
+ Fold07.Rep5: mtry= 5
- Fold07.Rep5: mtry= 5
+ Fold07.Rep5: mtry=10
- Fold07.Rep5: mtry=10
+ Fold07.Rep5: mtry=15
- Fold07.Rep5: mtry=15
+ Fold07.Rep5: mtry=20
- Fold07.Rep5: mtry=20
+ Fold07.Rep5: mtry=25
- Fold07.Rep5: mtry=25
+ Fold08.Rep5: mtry= 5
- Fold08.Rep5: mtry= 5
+ Fold08.Rep5: mtry=10
- Fold08.Rep5: mtry=10
+ Fold08.Rep5: mtry=15
- Fold08.Rep5: mtry=15
+ Fold08.Rep5: mtry=20
- Fold08.Rep5: mtry=20
+ Fold08.Rep5: mtry=25
- Fold08.Rep5: mtry=25
+ Fold09.Rep5: mtry= 5
- Fold09.Rep5: mtry= 5
+ Fold09.Rep5: mtry=10
- Fold09.Rep5: mtry=10
+ Fold09.Rep5: mtry=15
- Fold09.Rep5: mtry=15
+ Fold09.Rep5: mtry=20
- Fold09.Rep5: mtry=20
+ Fold09.Rep5: mtry=25
- Fold09.Rep5: mtry=25
+ Fold10.Rep5: mtry= 5
- Fold10.Rep5: mtry= 5
+ Fold10.Rep5: mtry=10
- Fold10.Rep5: mtry=10
+ Fold10.Rep5: mtry=15
- Fold10.Rep5: mtry=15
+ Fold10.Rep5: mtry=20
- Fold10.Rep5: mtry=20
+ Fold10.Rep5: mtry=25
- Fold10.Rep5: mtry=25
Aggregating results
Selecting tuning parameters
Fitting mtry = 10 on full training set
rfFit.yRandom Forest
891 samples
28 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Resampling results across tuning parameters:
mtry ROC Sens Spec
5 0.8842523 0.7122353 0.8969293
10 0.8885360 0.7286891 0.8940202
15 0.8874201 0.7351429 0.8878182
20 0.8864149 0.7415462 0.8867138
25 0.8866698 0.7427563 0.8816229
ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 10.
plot(rfFit.y)plot(rfFit.y$finalModel)densityplot(rfFit.y,pch='|')predict(rfFit.y,type = 'raw') -> train.rf.Class
predict(rfFit.y,type = 'prob') -> train.rf.Probs
histogram(~Survived+Dead,train.rf.Probs)rfsmoteFit.yRandom Forest
891 samples
28 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Addtional sampling using SMOTE
Resampling results across tuning parameters:
mtry ROC Sens Spec
5 0.8783818 0.6333782 0.9369966
10 0.8821756 0.6854286 0.9129697
15 0.8831274 0.6983025 0.9016498
20 0.8814043 0.7129580 0.8910976
25 0.8793606 0.7135462 0.8885455
ROC was used to select the optimal model using the largest value.
The final value used for the model was mtry = 15.
plot(rfsmoteFit.y)plot(rfsmoteFit.y$finalModel)densityplot(rfsmoteFit.y,pch='|')predict(rfsmoteFit.y,type = 'raw') -> train.rfsmoteFit.Class
predict(rfsmoteFit.y,type = 'prob') -> train.rfsmoteFit.Probs
histogram(~Survived+Dead,train.rfsmoteFit.Probs)ctrl <- trainControl(method = "repeatedcv",
repeats = 5,
verboseIter = T,
classProbs = TRUE,
summaryFunction = twoClassSummary,
sampling = 'smote'
)
glmnetGrid <- expand.grid(.alpha = c(0,.1,.2,.4,.6,.8,1),
.lambda = seq(0.01,0.2,length.out = 40))
set.seed(1)
dumV <- dummyVars(formula = Survived~.,data = train.imp)
Dtrain <- predict(dumV,train.imp)variable 'Survived' is not a factor
glmnetFit <- train(
x = Dtrain,
y = train.imp$Survived,
trControl=ctrl,
method='glmnet',
tuneGrid=glmnetGrid
)1 package is needed for this model and is not installed. (glmnet). Would you like to try to install it now?
1: yes
2: no
1trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.4/glmnet_2.0-10.tgz'
Content type 'application/x-gzip' length 1468512 bytes (1.4 MB)
==================================================
downloaded 1.4 MB
The downloaded binary packages are in
/var/folders/08/j649td5x1bgfxk70vw46jg880000gn/T//Rtmpcw7BTF/downloaded_packages
Loading required package: glmnet
Loading required package: Matrix
package ‘Matrix’ was built under R version 3.4.1
Attaching package: ‘Matrix’
The following object is masked from ‘package:tidyr’:
expand
Loading required package: foreach
foreach: simple, scalable parallel programming from Revolution Analytics
Use Revolution R for scalability, fault tolerance and more.
http://www.revolutionanalytics.com
Attaching package: ‘foreach’
The following objects are masked from ‘package:purrr’:
accumulate, when
Loaded glmnet 2.0-10
The metric "Accuracy" was not in the result set. ROC will be used instead.
+ Fold01.Rep1: alpha=0.0, lambda=0.2
- Fold01.Rep1: alpha=0.0, lambda=0.2
+ Fold01.Rep1: alpha=0.1, lambda=0.2
- Fold01.Rep1: alpha=0.1, lambda=0.2
+ Fold01.Rep1: alpha=0.2, lambda=0.2
- Fold01.Rep1: alpha=0.2, lambda=0.2
+ Fold01.Rep1: alpha=0.4, lambda=0.2
- Fold01.Rep1: alpha=0.4, lambda=0.2
+ Fold01.Rep1: alpha=0.6, lambda=0.2
- Fold01.Rep1: alpha=0.6, lambda=0.2
+ Fold01.Rep1: alpha=0.8, lambda=0.2
- Fold01.Rep1: alpha=0.8, lambda=0.2
+ Fold01.Rep1: alpha=1.0, lambda=0.2
- Fold01.Rep1: alpha=1.0, lambda=0.2
+ Fold02.Rep1: alpha=0.0, lambda=0.2
- Fold02.Rep1: alpha=0.0, lambda=0.2
+ Fold02.Rep1: alpha=0.1, lambda=0.2
- Fold02.Rep1: alpha=0.1, lambda=0.2
+ Fold02.Rep1: alpha=0.2, lambda=0.2
- Fold02.Rep1: alpha=0.2, lambda=0.2
+ Fold02.Rep1: alpha=0.4, lambda=0.2
- Fold02.Rep1: alpha=0.4, lambda=0.2
+ Fold02.Rep1: alpha=0.6, lambda=0.2
- Fold02.Rep1: alpha=0.6, lambda=0.2
+ Fold02.Rep1: alpha=0.8, lambda=0.2
- Fold02.Rep1: alpha=0.8, lambda=0.2
+ Fold02.Rep1: alpha=1.0, lambda=0.2
- Fold02.Rep1: alpha=1.0, lambda=0.2
+ Fold03.Rep1: alpha=0.0, lambda=0.2
- Fold03.Rep1: alpha=0.0, lambda=0.2
+ Fold03.Rep1: alpha=0.1, lambda=0.2
- Fold03.Rep1: alpha=0.1, lambda=0.2
+ Fold03.Rep1: alpha=0.2, lambda=0.2
- Fold03.Rep1: alpha=0.2, lambda=0.2
+ Fold03.Rep1: alpha=0.4, lambda=0.2
- Fold03.Rep1: alpha=0.4, lambda=0.2
+ Fold03.Rep1: alpha=0.6, lambda=0.2
- Fold03.Rep1: alpha=0.6, lambda=0.2
+ Fold03.Rep1: alpha=0.8, lambda=0.2
- Fold03.Rep1: alpha=0.8, lambda=0.2
+ Fold03.Rep1: alpha=1.0, lambda=0.2
- Fold03.Rep1: alpha=1.0, lambda=0.2
+ Fold04.Rep1: alpha=0.0, lambda=0.2
- Fold04.Rep1: alpha=0.0, lambda=0.2
+ Fold04.Rep1: alpha=0.1, lambda=0.2
- Fold04.Rep1: alpha=0.1, lambda=0.2
+ Fold04.Rep1: alpha=0.2, lambda=0.2
- Fold04.Rep1: alpha=0.2, lambda=0.2
+ Fold04.Rep1: alpha=0.4, lambda=0.2
- Fold04.Rep1: alpha=0.4, lambda=0.2
+ Fold04.Rep1: alpha=0.6, lambda=0.2
- Fold04.Rep1: alpha=0.6, lambda=0.2
+ Fold04.Rep1: alpha=0.8, lambda=0.2
- Fold04.Rep1: alpha=0.8, lambda=0.2
+ Fold04.Rep1: alpha=1.0, lambda=0.2
- Fold04.Rep1: alpha=1.0, lambda=0.2
+ Fold05.Rep1: alpha=0.0, lambda=0.2
- Fold05.Rep1: alpha=0.0, lambda=0.2
+ Fold05.Rep1: alpha=0.1, lambda=0.2
- Fold05.Rep1: alpha=0.1, lambda=0.2
+ Fold05.Rep1: alpha=0.2, lambda=0.2
- Fold05.Rep1: alpha=0.2, lambda=0.2
+ Fold05.Rep1: alpha=0.4, lambda=0.2
- Fold05.Rep1: alpha=0.4, lambda=0.2
+ Fold05.Rep1: alpha=0.6, lambda=0.2
- Fold05.Rep1: alpha=0.6, lambda=0.2
+ Fold05.Rep1: alpha=0.8, lambda=0.2
- Fold05.Rep1: alpha=0.8, lambda=0.2
+ Fold05.Rep1: alpha=1.0, lambda=0.2
- Fold05.Rep1: alpha=1.0, lambda=0.2
+ Fold06.Rep1: alpha=0.0, lambda=0.2
- Fold06.Rep1: alpha=0.0, lambda=0.2
+ Fold06.Rep1: alpha=0.1, lambda=0.2
- Fold06.Rep1: alpha=0.1, lambda=0.2
+ Fold06.Rep1: alpha=0.2, lambda=0.2
- Fold06.Rep1: alpha=0.2, lambda=0.2
+ Fold06.Rep1: alpha=0.4, lambda=0.2
- Fold06.Rep1: alpha=0.4, lambda=0.2
+ Fold06.Rep1: alpha=0.6, lambda=0.2
- Fold06.Rep1: alpha=0.6, lambda=0.2
+ Fold06.Rep1: alpha=0.8, lambda=0.2
- Fold06.Rep1: alpha=0.8, lambda=0.2
+ Fold06.Rep1: alpha=1.0, lambda=0.2
- Fold06.Rep1: alpha=1.0, lambda=0.2
+ Fold07.Rep1: alpha=0.0, lambda=0.2
- Fold07.Rep1: alpha=0.0, lambda=0.2
+ Fold07.Rep1: alpha=0.1, lambda=0.2
- Fold07.Rep1: alpha=0.1, lambda=0.2
+ Fold07.Rep1: alpha=0.2, lambda=0.2
- Fold07.Rep1: alpha=0.2, lambda=0.2
+ Fold07.Rep1: alpha=0.4, lambda=0.2
- Fold07.Rep1: alpha=0.4, lambda=0.2
+ Fold07.Rep1: alpha=0.6, lambda=0.2
- Fold07.Rep1: alpha=0.6, lambda=0.2
+ Fold07.Rep1: alpha=0.8, lambda=0.2
- Fold07.Rep1: alpha=0.8, lambda=0.2
+ Fold07.Rep1: alpha=1.0, lambda=0.2
- Fold07.Rep1: alpha=1.0, lambda=0.2
+ Fold08.Rep1: alpha=0.0, lambda=0.2
- Fold08.Rep1: alpha=0.0, lambda=0.2
+ Fold08.Rep1: alpha=0.1, lambda=0.2
- Fold08.Rep1: alpha=0.1, lambda=0.2
+ Fold08.Rep1: alpha=0.2, lambda=0.2
- Fold08.Rep1: alpha=0.2, lambda=0.2
+ Fold08.Rep1: alpha=0.4, lambda=0.2
- Fold08.Rep1: alpha=0.4, lambda=0.2
+ Fold08.Rep1: alpha=0.6, lambda=0.2
- Fold08.Rep1: alpha=0.6, lambda=0.2
+ Fold08.Rep1: alpha=0.8, lambda=0.2
- Fold08.Rep1: alpha=0.8, lambda=0.2
+ Fold08.Rep1: alpha=1.0, lambda=0.2
- Fold08.Rep1: alpha=1.0, lambda=0.2
+ Fold09.Rep1: alpha=0.0, lambda=0.2
- Fold09.Rep1: alpha=0.0, lambda=0.2
+ Fold09.Rep1: alpha=0.1, lambda=0.2
- Fold09.Rep1: alpha=0.1, lambda=0.2
+ Fold09.Rep1: alpha=0.2, lambda=0.2
- Fold09.Rep1: alpha=0.2, lambda=0.2
+ Fold09.Rep1: alpha=0.4, lambda=0.2
- Fold09.Rep1: alpha=0.4, lambda=0.2
+ Fold09.Rep1: alpha=0.6, lambda=0.2
- Fold09.Rep1: alpha=0.6, lambda=0.2
+ Fold09.Rep1: alpha=0.8, lambda=0.2
- Fold09.Rep1: alpha=0.8, lambda=0.2
+ Fold09.Rep1: alpha=1.0, lambda=0.2
- Fold09.Rep1: alpha=1.0, lambda=0.2
+ Fold10.Rep1: alpha=0.0, lambda=0.2
- Fold10.Rep1: alpha=0.0, lambda=0.2
+ Fold10.Rep1: alpha=0.1, lambda=0.2
- Fold10.Rep1: alpha=0.1, lambda=0.2
+ Fold10.Rep1: alpha=0.2, lambda=0.2
- Fold10.Rep1: alpha=0.2, lambda=0.2
+ Fold10.Rep1: alpha=0.4, lambda=0.2
- Fold10.Rep1: alpha=0.4, lambda=0.2
+ Fold10.Rep1: alpha=0.6, lambda=0.2
- Fold10.Rep1: alpha=0.6, lambda=0.2
+ Fold10.Rep1: alpha=0.8, lambda=0.2
- Fold10.Rep1: alpha=0.8, lambda=0.2
+ Fold10.Rep1: alpha=1.0, lambda=0.2
- Fold10.Rep1: alpha=1.0, lambda=0.2
+ Fold01.Rep2: alpha=0.0, lambda=0.2
- Fold01.Rep2: alpha=0.0, lambda=0.2
+ Fold01.Rep2: alpha=0.1, lambda=0.2
- Fold01.Rep2: alpha=0.1, lambda=0.2
+ Fold01.Rep2: alpha=0.2, lambda=0.2
- Fold01.Rep2: alpha=0.2, lambda=0.2
+ Fold01.Rep2: alpha=0.4, lambda=0.2
- Fold01.Rep2: alpha=0.4, lambda=0.2
+ Fold01.Rep2: alpha=0.6, lambda=0.2
- Fold01.Rep2: alpha=0.6, lambda=0.2
+ Fold01.Rep2: alpha=0.8, lambda=0.2
- Fold01.Rep2: alpha=0.8, lambda=0.2
+ Fold01.Rep2: alpha=1.0, lambda=0.2
- Fold01.Rep2: alpha=1.0, lambda=0.2
+ Fold02.Rep2: alpha=0.0, lambda=0.2
- Fold02.Rep2: alpha=0.0, lambda=0.2
+ Fold02.Rep2: alpha=0.1, lambda=0.2
- Fold02.Rep2: alpha=0.1, lambda=0.2
+ Fold02.Rep2: alpha=0.2, lambda=0.2
- Fold02.Rep2: alpha=0.2, lambda=0.2
+ Fold02.Rep2: alpha=0.4, lambda=0.2
- Fold02.Rep2: alpha=0.4, lambda=0.2
+ Fold02.Rep2: alpha=0.6, lambda=0.2
- Fold02.Rep2: alpha=0.6, lambda=0.2
+ Fold02.Rep2: alpha=0.8, lambda=0.2
- Fold02.Rep2: alpha=0.8, lambda=0.2
+ Fold02.Rep2: alpha=1.0, lambda=0.2
- Fold02.Rep2: alpha=1.0, lambda=0.2
+ Fold03.Rep2: alpha=0.0, lambda=0.2
- Fold03.Rep2: alpha=0.0, lambda=0.2
+ Fold03.Rep2: alpha=0.1, lambda=0.2
- Fold03.Rep2: alpha=0.1, lambda=0.2
+ Fold03.Rep2: alpha=0.2, lambda=0.2
- Fold03.Rep2: alpha=0.2, lambda=0.2
+ Fold03.Rep2: alpha=0.4, lambda=0.2
- Fold03.Rep2: alpha=0.4, lambda=0.2
+ Fold03.Rep2: alpha=0.6, lambda=0.2
- Fold03.Rep2: alpha=0.6, lambda=0.2
+ Fold03.Rep2: alpha=0.8, lambda=0.2
- Fold03.Rep2: alpha=0.8, lambda=0.2
+ Fold03.Rep2: alpha=1.0, lambda=0.2
- Fold03.Rep2: alpha=1.0, lambda=0.2
+ Fold04.Rep2: alpha=0.0, lambda=0.2
- Fold04.Rep2: alpha=0.0, lambda=0.2
+ Fold04.Rep2: alpha=0.1, lambda=0.2
- Fold04.Rep2: alpha=0.1, lambda=0.2
+ Fold04.Rep2: alpha=0.2, lambda=0.2
- Fold04.Rep2: alpha=0.2, lambda=0.2
+ Fold04.Rep2: alpha=0.4, lambda=0.2
- Fold04.Rep2: alpha=0.4, lambda=0.2
+ Fold04.Rep2: alpha=0.6, lambda=0.2
- Fold04.Rep2: alpha=0.6, lambda=0.2
+ Fold04.Rep2: alpha=0.8, lambda=0.2
- Fold04.Rep2: alpha=0.8, lambda=0.2
+ Fold04.Rep2: alpha=1.0, lambda=0.2
- Fold04.Rep2: alpha=1.0, lambda=0.2
+ Fold05.Rep2: alpha=0.0, lambda=0.2
- Fold05.Rep2: alpha=0.0, lambda=0.2
+ Fold05.Rep2: alpha=0.1, lambda=0.2
- Fold05.Rep2: alpha=0.1, lambda=0.2
+ Fold05.Rep2: alpha=0.2, lambda=0.2
- Fold05.Rep2: alpha=0.2, lambda=0.2
+ Fold05.Rep2: alpha=0.4, lambda=0.2
- Fold05.Rep2: alpha=0.4, lambda=0.2
+ Fold05.Rep2: alpha=0.6, lambda=0.2
- Fold05.Rep2: alpha=0.6, lambda=0.2
+ Fold05.Rep2: alpha=0.8, lambda=0.2
- Fold05.Rep2: alpha=0.8, lambda=0.2
+ Fold05.Rep2: alpha=1.0, lambda=0.2
- Fold05.Rep2: alpha=1.0, lambda=0.2
+ Fold06.Rep2: alpha=0.0, lambda=0.2
- Fold06.Rep2: alpha=0.0, lambda=0.2
+ Fold06.Rep2: alpha=0.1, lambda=0.2
- Fold06.Rep2: alpha=0.1, lambda=0.2
+ Fold06.Rep2: alpha=0.2, lambda=0.2
- Fold06.Rep2: alpha=0.2, lambda=0.2
+ Fold06.Rep2: alpha=0.4, lambda=0.2
- Fold06.Rep2: alpha=0.4, lambda=0.2
+ Fold06.Rep2: alpha=0.6, lambda=0.2
- Fold06.Rep2: alpha=0.6, lambda=0.2
+ Fold06.Rep2: alpha=0.8, lambda=0.2
- Fold06.Rep2: alpha=0.8, lambda=0.2
+ Fold06.Rep2: alpha=1.0, lambda=0.2
- Fold06.Rep2: alpha=1.0, lambda=0.2
+ Fold07.Rep2: alpha=0.0, lambda=0.2
- Fold07.Rep2: alpha=0.0, lambda=0.2
+ Fold07.Rep2: alpha=0.1, lambda=0.2
- Fold07.Rep2: alpha=0.1, lambda=0.2
+ Fold07.Rep2: alpha=0.2, lambda=0.2
- Fold07.Rep2: alpha=0.2, lambda=0.2
+ Fold07.Rep2: alpha=0.4, lambda=0.2
- Fold07.Rep2: alpha=0.4, lambda=0.2
+ Fold07.Rep2: alpha=0.6, lambda=0.2
- Fold07.Rep2: alpha=0.6, lambda=0.2
+ Fold07.Rep2: alpha=0.8, lambda=0.2
- Fold07.Rep2: alpha=0.8, lambda=0.2
+ Fold07.Rep2: alpha=1.0, lambda=0.2
- Fold07.Rep2: alpha=1.0, lambda=0.2
+ Fold08.Rep2: alpha=0.0, lambda=0.2
- Fold08.Rep2: alpha=0.0, lambda=0.2
+ Fold08.Rep2: alpha=0.1, lambda=0.2
- Fold08.Rep2: alpha=0.1, lambda=0.2
+ Fold08.Rep2: alpha=0.2, lambda=0.2
- Fold08.Rep2: alpha=0.2, lambda=0.2
+ Fold08.Rep2: alpha=0.4, lambda=0.2
- Fold08.Rep2: alpha=0.4, lambda=0.2
+ Fold08.Rep2: alpha=0.6, lambda=0.2
- Fold08.Rep2: alpha=0.6, lambda=0.2
+ Fold08.Rep2: alpha=0.8, lambda=0.2
- Fold08.Rep2: alpha=0.8, lambda=0.2
+ Fold08.Rep2: alpha=1.0, lambda=0.2
- Fold08.Rep2: alpha=1.0, lambda=0.2
+ Fold09.Rep2: alpha=0.0, lambda=0.2
- Fold09.Rep2: alpha=0.0, lambda=0.2
+ Fold09.Rep2: alpha=0.1, lambda=0.2
- Fold09.Rep2: alpha=0.1, lambda=0.2
+ Fold09.Rep2: alpha=0.2, lambda=0.2
- Fold09.Rep2: alpha=0.2, lambda=0.2
+ Fold09.Rep2: alpha=0.4, lambda=0.2
- Fold09.Rep2: alpha=0.4, lambda=0.2
+ Fold09.Rep2: alpha=0.6, lambda=0.2
- Fold09.Rep2: alpha=0.6, lambda=0.2
+ Fold09.Rep2: alpha=0.8, lambda=0.2
- Fold09.Rep2: alpha=0.8, lambda=0.2
+ Fold09.Rep2: alpha=1.0, lambda=0.2
- Fold09.Rep2: alpha=1.0, lambda=0.2
+ Fold10.Rep2: alpha=0.0, lambda=0.2
- Fold10.Rep2: alpha=0.0, lambda=0.2
+ Fold10.Rep2: alpha=0.1, lambda=0.2
- Fold10.Rep2: alpha=0.1, lambda=0.2
+ Fold10.Rep2: alpha=0.2, lambda=0.2
- Fold10.Rep2: alpha=0.2, lambda=0.2
+ Fold10.Rep2: alpha=0.4, lambda=0.2
- Fold10.Rep2: alpha=0.4, lambda=0.2
+ Fold10.Rep2: alpha=0.6, lambda=0.2
- Fold10.Rep2: alpha=0.6, lambda=0.2
+ Fold10.Rep2: alpha=0.8, lambda=0.2
- Fold10.Rep2: alpha=0.8, lambda=0.2
+ Fold10.Rep2: alpha=1.0, lambda=0.2
- Fold10.Rep2: alpha=1.0, lambda=0.2
+ Fold01.Rep3: alpha=0.0, lambda=0.2
- Fold01.Rep3: alpha=0.0, lambda=0.2
+ Fold01.Rep3: alpha=0.1, lambda=0.2
- Fold01.Rep3: alpha=0.1, lambda=0.2
+ Fold01.Rep3: alpha=0.2, lambda=0.2
- Fold01.Rep3: alpha=0.2, lambda=0.2
+ Fold01.Rep3: alpha=0.4, lambda=0.2
- Fold01.Rep3: alpha=0.4, lambda=0.2
+ Fold01.Rep3: alpha=0.6, lambda=0.2
- Fold01.Rep3: alpha=0.6, lambda=0.2
+ Fold01.Rep3: alpha=0.8, lambda=0.2
- Fold01.Rep3: alpha=0.8, lambda=0.2
+ Fold01.Rep3: alpha=1.0, lambda=0.2
- Fold01.Rep3: alpha=1.0, lambda=0.2
+ Fold02.Rep3: alpha=0.0, lambda=0.2
- Fold02.Rep3: alpha=0.0, lambda=0.2
+ Fold02.Rep3: alpha=0.1, lambda=0.2
- Fold02.Rep3: alpha=0.1, lambda=0.2
+ Fold02.Rep3: alpha=0.2, lambda=0.2
- Fold02.Rep3: alpha=0.2, lambda=0.2
+ Fold02.Rep3: alpha=0.4, lambda=0.2
- Fold02.Rep3: alpha=0.4, lambda=0.2
+ Fold02.Rep3: alpha=0.6, lambda=0.2
- Fold02.Rep3: alpha=0.6, lambda=0.2
+ Fold02.Rep3: alpha=0.8, lambda=0.2
- Fold02.Rep3: alpha=0.8, lambda=0.2
+ Fold02.Rep3: alpha=1.0, lambda=0.2
- Fold02.Rep3: alpha=1.0, lambda=0.2
+ Fold03.Rep3: alpha=0.0, lambda=0.2
- Fold03.Rep3: alpha=0.0, lambda=0.2
+ Fold03.Rep3: alpha=0.1, lambda=0.2
- Fold03.Rep3: alpha=0.1, lambda=0.2
+ Fold03.Rep3: alpha=0.2, lambda=0.2
- Fold03.Rep3: alpha=0.2, lambda=0.2
+ Fold03.Rep3: alpha=0.4, lambda=0.2
- Fold03.Rep3: alpha=0.4, lambda=0.2
+ Fold03.Rep3: alpha=0.6, lambda=0.2
- Fold03.Rep3: alpha=0.6, lambda=0.2
+ Fold03.Rep3: alpha=0.8, lambda=0.2
- Fold03.Rep3: alpha=0.8, lambda=0.2
+ Fold03.Rep3: alpha=1.0, lambda=0.2
- Fold03.Rep3: alpha=1.0, lambda=0.2
+ Fold04.Rep3: alpha=0.0, lambda=0.2
- Fold04.Rep3: alpha=0.0, lambda=0.2
+ Fold04.Rep3: alpha=0.1, lambda=0.2
- Fold04.Rep3: alpha=0.1, lambda=0.2
+ Fold04.Rep3: alpha=0.2, lambda=0.2
- Fold04.Rep3: alpha=0.2, lambda=0.2
+ Fold04.Rep3: alpha=0.4, lambda=0.2
- Fold04.Rep3: alpha=0.4, lambda=0.2
+ Fold04.Rep3: alpha=0.6, lambda=0.2
- Fold04.Rep3: alpha=0.6, lambda=0.2
+ Fold04.Rep3: alpha=0.8, lambda=0.2
- Fold04.Rep3: alpha=0.8, lambda=0.2
+ Fold04.Rep3: alpha=1.0, lambda=0.2
- Fold04.Rep3: alpha=1.0, lambda=0.2
+ Fold05.Rep3: alpha=0.0, lambda=0.2
- Fold05.Rep3: alpha=0.0, lambda=0.2
+ Fold05.Rep3: alpha=0.1, lambda=0.2
- Fold05.Rep3: alpha=0.1, lambda=0.2
+ Fold05.Rep3: alpha=0.2, lambda=0.2
- Fold05.Rep3: alpha=0.2, lambda=0.2
+ Fold05.Rep3: alpha=0.4, lambda=0.2
- Fold05.Rep3: alpha=0.4, lambda=0.2
+ Fold05.Rep3: alpha=0.6, lambda=0.2
- Fold05.Rep3: alpha=0.6, lambda=0.2
+ Fold05.Rep3: alpha=0.8, lambda=0.2
- Fold05.Rep3: alpha=0.8, lambda=0.2
+ Fold05.Rep3: alpha=1.0, lambda=0.2
- Fold05.Rep3: alpha=1.0, lambda=0.2
+ Fold06.Rep3: alpha=0.0, lambda=0.2
- Fold06.Rep3: alpha=0.0, lambda=0.2
+ Fold06.Rep3: alpha=0.1, lambda=0.2
- Fold06.Rep3: alpha=0.1, lambda=0.2
+ Fold06.Rep3: alpha=0.2, lambda=0.2
- Fold06.Rep3: alpha=0.2, lambda=0.2
+ Fold06.Rep3: alpha=0.4, lambda=0.2
- Fold06.Rep3: alpha=0.4, lambda=0.2
+ Fold06.Rep3: alpha=0.6, lambda=0.2
- Fold06.Rep3: alpha=0.6, lambda=0.2
+ Fold06.Rep3: alpha=0.8, lambda=0.2
- Fold06.Rep3: alpha=0.8, lambda=0.2
+ Fold06.Rep3: alpha=1.0, lambda=0.2
- Fold06.Rep3: alpha=1.0, lambda=0.2
+ Fold07.Rep3: alpha=0.0, lambda=0.2
- Fold07.Rep3: alpha=0.0, lambda=0.2
+ Fold07.Rep3: alpha=0.1, lambda=0.2
- Fold07.Rep3: alpha=0.1, lambda=0.2
+ Fold07.Rep3: alpha=0.2, lambda=0.2
- Fold07.Rep3: alpha=0.2, lambda=0.2
+ Fold07.Rep3: alpha=0.4, lambda=0.2
- Fold07.Rep3: alpha=0.4, lambda=0.2
+ Fold07.Rep3: alpha=0.6, lambda=0.2
- Fold07.Rep3: alpha=0.6, lambda=0.2
+ Fold07.Rep3: alpha=0.8, lambda=0.2
- Fold07.Rep3: alpha=0.8, lambda=0.2
+ Fold07.Rep3: alpha=1.0, lambda=0.2
- Fold07.Rep3: alpha=1.0, lambda=0.2
+ Fold08.Rep3: alpha=0.0, lambda=0.2
- Fold08.Rep3: alpha=0.0, lambda=0.2
+ Fold08.Rep3: alpha=0.1, lambda=0.2
- Fold08.Rep3: alpha=0.1, lambda=0.2
+ Fold08.Rep3: alpha=0.2, lambda=0.2
- Fold08.Rep3: alpha=0.2, lambda=0.2
+ Fold08.Rep3: alpha=0.4, lambda=0.2
- Fold08.Rep3: alpha=0.4, lambda=0.2
+ Fold08.Rep3: alpha=0.6, lambda=0.2
- Fold08.Rep3: alpha=0.6, lambda=0.2
+ Fold08.Rep3: alpha=0.8, lambda=0.2
- Fold08.Rep3: alpha=0.8, lambda=0.2
+ Fold08.Rep3: alpha=1.0, lambda=0.2
- Fold08.Rep3: alpha=1.0, lambda=0.2
+ Fold09.Rep3: alpha=0.0, lambda=0.2
- Fold09.Rep3: alpha=0.0, lambda=0.2
+ Fold09.Rep3: alpha=0.1, lambda=0.2
- Fold09.Rep3: alpha=0.1, lambda=0.2
+ Fold09.Rep3: alpha=0.2, lambda=0.2
- Fold09.Rep3: alpha=0.2, lambda=0.2
+ Fold09.Rep3: alpha=0.4, lambda=0.2
- Fold09.Rep3: alpha=0.4, lambda=0.2
+ Fold09.Rep3: alpha=0.6, lambda=0.2
- Fold09.Rep3: alpha=0.6, lambda=0.2
+ Fold09.Rep3: alpha=0.8, lambda=0.2
- Fold09.Rep3: alpha=0.8, lambda=0.2
+ Fold09.Rep3: alpha=1.0, lambda=0.2
- Fold09.Rep3: alpha=1.0, lambda=0.2
+ Fold10.Rep3: alpha=0.0, lambda=0.2
- Fold10.Rep3: alpha=0.0, lambda=0.2
+ Fold10.Rep3: alpha=0.1, lambda=0.2
- Fold10.Rep3: alpha=0.1, lambda=0.2
+ Fold10.Rep3: alpha=0.2, lambda=0.2
- Fold10.Rep3: alpha=0.2, lambda=0.2
+ Fold10.Rep3: alpha=0.4, lambda=0.2
- Fold10.Rep3: alpha=0.4, lambda=0.2
+ Fold10.Rep3: alpha=0.6, lambda=0.2
- Fold10.Rep3: alpha=0.6, lambda=0.2
+ Fold10.Rep3: alpha=0.8, lambda=0.2
- Fold10.Rep3: alpha=0.8, lambda=0.2
+ Fold10.Rep3: alpha=1.0, lambda=0.2
- Fold10.Rep3: alpha=1.0, lambda=0.2
+ Fold01.Rep4: alpha=0.0, lambda=0.2
- Fold01.Rep4: alpha=0.0, lambda=0.2
+ Fold01.Rep4: alpha=0.1, lambda=0.2
- Fold01.Rep4: alpha=0.1, lambda=0.2
+ Fold01.Rep4: alpha=0.2, lambda=0.2
- Fold01.Rep4: alpha=0.2, lambda=0.2
+ Fold01.Rep4: alpha=0.4, lambda=0.2
- Fold01.Rep4: alpha=0.4, lambda=0.2
+ Fold01.Rep4: alpha=0.6, lambda=0.2
- Fold01.Rep4: alpha=0.6, lambda=0.2
+ Fold01.Rep4: alpha=0.8, lambda=0.2
- Fold01.Rep4: alpha=0.8, lambda=0.2
+ Fold01.Rep4: alpha=1.0, lambda=0.2
- Fold01.Rep4: alpha=1.0, lambda=0.2
+ Fold02.Rep4: alpha=0.0, lambda=0.2
- Fold02.Rep4: alpha=0.0, lambda=0.2
+ Fold02.Rep4: alpha=0.1, lambda=0.2
- Fold02.Rep4: alpha=0.1, lambda=0.2
+ Fold02.Rep4: alpha=0.2, lambda=0.2
- Fold02.Rep4: alpha=0.2, lambda=0.2
+ Fold02.Rep4: alpha=0.4, lambda=0.2
- Fold02.Rep4: alpha=0.4, lambda=0.2
+ Fold02.Rep4: alpha=0.6, lambda=0.2
- Fold02.Rep4: alpha=0.6, lambda=0.2
+ Fold02.Rep4: alpha=0.8, lambda=0.2
- Fold02.Rep4: alpha=0.8, lambda=0.2
+ Fold02.Rep4: alpha=1.0, lambda=0.2
- Fold02.Rep4: alpha=1.0, lambda=0.2
+ Fold03.Rep4: alpha=0.0, lambda=0.2
- Fold03.Rep4: alpha=0.0, lambda=0.2
+ Fold03.Rep4: alpha=0.1, lambda=0.2
- Fold03.Rep4: alpha=0.1, lambda=0.2
+ Fold03.Rep4: alpha=0.2, lambda=0.2
- Fold03.Rep4: alpha=0.2, lambda=0.2
+ Fold03.Rep4: alpha=0.4, lambda=0.2
- Fold03.Rep4: alpha=0.4, lambda=0.2
+ Fold03.Rep4: alpha=0.6, lambda=0.2
- Fold03.Rep4: alpha=0.6, lambda=0.2
+ Fold03.Rep4: alpha=0.8, lambda=0.2
- Fold03.Rep4: alpha=0.8, lambda=0.2
+ Fold03.Rep4: alpha=1.0, lambda=0.2
- Fold03.Rep4: alpha=1.0, lambda=0.2
+ Fold04.Rep4: alpha=0.0, lambda=0.2
- Fold04.Rep4: alpha=0.0, lambda=0.2
+ Fold04.Rep4: alpha=0.1, lambda=0.2
- Fold04.Rep4: alpha=0.1, lambda=0.2
+ Fold04.Rep4: alpha=0.2, lambda=0.2
- Fold04.Rep4: alpha=0.2, lambda=0.2
+ Fold04.Rep4: alpha=0.4, lambda=0.2
- Fold04.Rep4: alpha=0.4, lambda=0.2
+ Fold04.Rep4: alpha=0.6, lambda=0.2
- Fold04.Rep4: alpha=0.6, lambda=0.2
+ Fold04.Rep4: alpha=0.8, lambda=0.2
- Fold04.Rep4: alpha=0.8, lambda=0.2
+ Fold04.Rep4: alpha=1.0, lambda=0.2
- Fold04.Rep4: alpha=1.0, lambda=0.2
+ Fold05.Rep4: alpha=0.0, lambda=0.2
- Fold05.Rep4: alpha=0.0, lambda=0.2
+ Fold05.Rep4: alpha=0.1, lambda=0.2
- Fold05.Rep4: alpha=0.1, lambda=0.2
+ Fold05.Rep4: alpha=0.2, lambda=0.2
- Fold05.Rep4: alpha=0.2, lambda=0.2
+ Fold05.Rep4: alpha=0.4, lambda=0.2
- Fold05.Rep4: alpha=0.4, lambda=0.2
+ Fold05.Rep4: alpha=0.6, lambda=0.2
- Fold05.Rep4: alpha=0.6, lambda=0.2
+ Fold05.Rep4: alpha=0.8, lambda=0.2
- Fold05.Rep4: alpha=0.8, lambda=0.2
+ Fold05.Rep4: alpha=1.0, lambda=0.2
- Fold05.Rep4: alpha=1.0, lambda=0.2
+ Fold06.Rep4: alpha=0.0, lambda=0.2
- Fold06.Rep4: alpha=0.0, lambda=0.2
+ Fold06.Rep4: alpha=0.1, lambda=0.2
- Fold06.Rep4: alpha=0.1, lambda=0.2
+ Fold06.Rep4: alpha=0.2, lambda=0.2
- Fold06.Rep4: alpha=0.2, lambda=0.2
+ Fold06.Rep4: alpha=0.4, lambda=0.2
- Fold06.Rep4: alpha=0.4, lambda=0.2
+ Fold06.Rep4: alpha=0.6, lambda=0.2
- Fold06.Rep4: alpha=0.6, lambda=0.2
+ Fold06.Rep4: alpha=0.8, lambda=0.2
- Fold06.Rep4: alpha=0.8, lambda=0.2
+ Fold06.Rep4: alpha=1.0, lambda=0.2
- Fold06.Rep4: alpha=1.0, lambda=0.2
+ Fold07.Rep4: alpha=0.0, lambda=0.2
- Fold07.Rep4: alpha=0.0, lambda=0.2
+ Fold07.Rep4: alpha=0.1, lambda=0.2
- Fold07.Rep4: alpha=0.1, lambda=0.2
+ Fold07.Rep4: alpha=0.2, lambda=0.2
- Fold07.Rep4: alpha=0.2, lambda=0.2
+ Fold07.Rep4: alpha=0.4, lambda=0.2
- Fold07.Rep4: alpha=0.4, lambda=0.2
+ Fold07.Rep4: alpha=0.6, lambda=0.2
- Fold07.Rep4: alpha=0.6, lambda=0.2
+ Fold07.Rep4: alpha=0.8, lambda=0.2
- Fold07.Rep4: alpha=0.8, lambda=0.2
+ Fold07.Rep4: alpha=1.0, lambda=0.2
- Fold07.Rep4: alpha=1.0, lambda=0.2
+ Fold08.Rep4: alpha=0.0, lambda=0.2
- Fold08.Rep4: alpha=0.0, lambda=0.2
+ Fold08.Rep4: alpha=0.1, lambda=0.2
- Fold08.Rep4: alpha=0.1, lambda=0.2
+ Fold08.Rep4: alpha=0.2, lambda=0.2
- Fold08.Rep4: alpha=0.2, lambda=0.2
+ Fold08.Rep4: alpha=0.4, lambda=0.2
- Fold08.Rep4: alpha=0.4, lambda=0.2
+ Fold08.Rep4: alpha=0.6, lambda=0.2
- Fold08.Rep4: alpha=0.6, lambda=0.2
+ Fold08.Rep4: alpha=0.8, lambda=0.2
- Fold08.Rep4: alpha=0.8, lambda=0.2
+ Fold08.Rep4: alpha=1.0, lambda=0.2
- Fold08.Rep4: alpha=1.0, lambda=0.2
+ Fold09.Rep4: alpha=0.0, lambda=0.2
- Fold09.Rep4: alpha=0.0, lambda=0.2
+ Fold09.Rep4: alpha=0.1, lambda=0.2
- Fold09.Rep4: alpha=0.1, lambda=0.2
+ Fold09.Rep4: alpha=0.2, lambda=0.2
- Fold09.Rep4: alpha=0.2, lambda=0.2
+ Fold09.Rep4: alpha=0.4, lambda=0.2
- Fold09.Rep4: alpha=0.4, lambda=0.2
+ Fold09.Rep4: alpha=0.6, lambda=0.2
- Fold09.Rep4: alpha=0.6, lambda=0.2
+ Fold09.Rep4: alpha=0.8, lambda=0.2
- Fold09.Rep4: alpha=0.8, lambda=0.2
+ Fold09.Rep4: alpha=1.0, lambda=0.2
- Fold09.Rep4: alpha=1.0, lambda=0.2
+ Fold10.Rep4: alpha=0.0, lambda=0.2
- Fold10.Rep4: alpha=0.0, lambda=0.2
+ Fold10.Rep4: alpha=0.1, lambda=0.2
- Fold10.Rep4: alpha=0.1, lambda=0.2
+ Fold10.Rep4: alpha=0.2, lambda=0.2
- Fold10.Rep4: alpha=0.2, lambda=0.2
+ Fold10.Rep4: alpha=0.4, lambda=0.2
- Fold10.Rep4: alpha=0.4, lambda=0.2
+ Fold10.Rep4: alpha=0.6, lambda=0.2
- Fold10.Rep4: alpha=0.6, lambda=0.2
+ Fold10.Rep4: alpha=0.8, lambda=0.2
- Fold10.Rep4: alpha=0.8, lambda=0.2
+ Fold10.Rep4: alpha=1.0, lambda=0.2
- Fold10.Rep4: alpha=1.0, lambda=0.2
+ Fold01.Rep5: alpha=0.0, lambda=0.2
- Fold01.Rep5: alpha=0.0, lambda=0.2
+ Fold01.Rep5: alpha=0.1, lambda=0.2
- Fold01.Rep5: alpha=0.1, lambda=0.2
+ Fold01.Rep5: alpha=0.2, lambda=0.2
- Fold01.Rep5: alpha=0.2, lambda=0.2
+ Fold01.Rep5: alpha=0.4, lambda=0.2
- Fold01.Rep5: alpha=0.4, lambda=0.2
+ Fold01.Rep5: alpha=0.6, lambda=0.2
- Fold01.Rep5: alpha=0.6, lambda=0.2
+ Fold01.Rep5: alpha=0.8, lambda=0.2
- Fold01.Rep5: alpha=0.8, lambda=0.2
+ Fold01.Rep5: alpha=1.0, lambda=0.2
- Fold01.Rep5: alpha=1.0, lambda=0.2
+ Fold02.Rep5: alpha=0.0, lambda=0.2
- Fold02.Rep5: alpha=0.0, lambda=0.2
+ Fold02.Rep5: alpha=0.1, lambda=0.2
- Fold02.Rep5: alpha=0.1, lambda=0.2
+ Fold02.Rep5: alpha=0.2, lambda=0.2
- Fold02.Rep5: alpha=0.2, lambda=0.2
+ Fold02.Rep5: alpha=0.4, lambda=0.2
- Fold02.Rep5: alpha=0.4, lambda=0.2
+ Fold02.Rep5: alpha=0.6, lambda=0.2
- Fold02.Rep5: alpha=0.6, lambda=0.2
+ Fold02.Rep5: alpha=0.8, lambda=0.2
- Fold02.Rep5: alpha=0.8, lambda=0.2
+ Fold02.Rep5: alpha=1.0, lambda=0.2
- Fold02.Rep5: alpha=1.0, lambda=0.2
+ Fold03.Rep5: alpha=0.0, lambda=0.2
- Fold03.Rep5: alpha=0.0, lambda=0.2
+ Fold03.Rep5: alpha=0.1, lambda=0.2
- Fold03.Rep5: alpha=0.1, lambda=0.2
+ Fold03.Rep5: alpha=0.2, lambda=0.2
- Fold03.Rep5: alpha=0.2, lambda=0.2
+ Fold03.Rep5: alpha=0.4, lambda=0.2
- Fold03.Rep5: alpha=0.4, lambda=0.2
+ Fold03.Rep5: alpha=0.6, lambda=0.2
- Fold03.Rep5: alpha=0.6, lambda=0.2
+ Fold03.Rep5: alpha=0.8, lambda=0.2
- Fold03.Rep5: alpha=0.8, lambda=0.2
+ Fold03.Rep5: alpha=1.0, lambda=0.2
- Fold03.Rep5: alpha=1.0, lambda=0.2
+ Fold04.Rep5: alpha=0.0, lambda=0.2
- Fold04.Rep5: alpha=0.0, lambda=0.2
+ Fold04.Rep5: alpha=0.1, lambda=0.2
- Fold04.Rep5: alpha=0.1, lambda=0.2
+ Fold04.Rep5: alpha=0.2, lambda=0.2
- Fold04.Rep5: alpha=0.2, lambda=0.2
+ Fold04.Rep5: alpha=0.4, lambda=0.2
- Fold04.Rep5: alpha=0.4, lambda=0.2
+ Fold04.Rep5: alpha=0.6, lambda=0.2
- Fold04.Rep5: alpha=0.6, lambda=0.2
+ Fold04.Rep5: alpha=0.8, lambda=0.2
- Fold04.Rep5: alpha=0.8, lambda=0.2
+ Fold04.Rep5: alpha=1.0, lambda=0.2
- Fold04.Rep5: alpha=1.0, lambda=0.2
+ Fold05.Rep5: alpha=0.0, lambda=0.2
- Fold05.Rep5: alpha=0.0, lambda=0.2
+ Fold05.Rep5: alpha=0.1, lambda=0.2
- Fold05.Rep5: alpha=0.1, lambda=0.2
+ Fold05.Rep5: alpha=0.2, lambda=0.2
- Fold05.Rep5: alpha=0.2, lambda=0.2
+ Fold05.Rep5: alpha=0.4, lambda=0.2
- Fold05.Rep5: alpha=0.4, lambda=0.2
+ Fold05.Rep5: alpha=0.6, lambda=0.2
- Fold05.Rep5: alpha=0.6, lambda=0.2
+ Fold05.Rep5: alpha=0.8, lambda=0.2
- Fold05.Rep5: alpha=0.8, lambda=0.2
+ Fold05.Rep5: alpha=1.0, lambda=0.2
- Fold05.Rep5: alpha=1.0, lambda=0.2
+ Fold06.Rep5: alpha=0.0, lambda=0.2
- Fold06.Rep5: alpha=0.0, lambda=0.2
+ Fold06.Rep5: alpha=0.1, lambda=0.2
- Fold06.Rep5: alpha=0.1, lambda=0.2
+ Fold06.Rep5: alpha=0.2, lambda=0.2
- Fold06.Rep5: alpha=0.2, lambda=0.2
+ Fold06.Rep5: alpha=0.4, lambda=0.2
- Fold06.Rep5: alpha=0.4, lambda=0.2
+ Fold06.Rep5: alpha=0.6, lambda=0.2
- Fold06.Rep5: alpha=0.6, lambda=0.2
+ Fold06.Rep5: alpha=0.8, lambda=0.2
- Fold06.Rep5: alpha=0.8, lambda=0.2
+ Fold06.Rep5: alpha=1.0, lambda=0.2
- Fold06.Rep5: alpha=1.0, lambda=0.2
+ Fold07.Rep5: alpha=0.0, lambda=0.2
- Fold07.Rep5: alpha=0.0, lambda=0.2
+ Fold07.Rep5: alpha=0.1, lambda=0.2
- Fold07.Rep5: alpha=0.1, lambda=0.2
+ Fold07.Rep5: alpha=0.2, lambda=0.2
- Fold07.Rep5: alpha=0.2, lambda=0.2
+ Fold07.Rep5: alpha=0.4, lambda=0.2
- Fold07.Rep5: alpha=0.4, lambda=0.2
+ Fold07.Rep5: alpha=0.6, lambda=0.2
- Fold07.Rep5: alpha=0.6, lambda=0.2
+ Fold07.Rep5: alpha=0.8, lambda=0.2
- Fold07.Rep5: alpha=0.8, lambda=0.2
+ Fold07.Rep5: alpha=1.0, lambda=0.2
- Fold07.Rep5: alpha=1.0, lambda=0.2
+ Fold08.Rep5: alpha=0.0, lambda=0.2
- Fold08.Rep5: alpha=0.0, lambda=0.2
+ Fold08.Rep5: alpha=0.1, lambda=0.2
- Fold08.Rep5: alpha=0.1, lambda=0.2
+ Fold08.Rep5: alpha=0.2, lambda=0.2
- Fold08.Rep5: alpha=0.2, lambda=0.2
+ Fold08.Rep5: alpha=0.4, lambda=0.2
- Fold08.Rep5: alpha=0.4, lambda=0.2
+ Fold08.Rep5: alpha=0.6, lambda=0.2
- Fold08.Rep5: alpha=0.6, lambda=0.2
+ Fold08.Rep5: alpha=0.8, lambda=0.2
- Fold08.Rep5: alpha=0.8, lambda=0.2
+ Fold08.Rep5: alpha=1.0, lambda=0.2
- Fold08.Rep5: alpha=1.0, lambda=0.2
+ Fold09.Rep5: alpha=0.0, lambda=0.2
- Fold09.Rep5: alpha=0.0, lambda=0.2
+ Fold09.Rep5: alpha=0.1, lambda=0.2
- Fold09.Rep5: alpha=0.1, lambda=0.2
+ Fold09.Rep5: alpha=0.2, lambda=0.2
- Fold09.Rep5: alpha=0.2, lambda=0.2
+ Fold09.Rep5: alpha=0.4, lambda=0.2
- Fold09.Rep5: alpha=0.4, lambda=0.2
+ Fold09.Rep5: alpha=0.6, lambda=0.2
- Fold09.Rep5: alpha=0.6, lambda=0.2
+ Fold09.Rep5: alpha=0.8, lambda=0.2
- Fold09.Rep5: alpha=0.8, lambda=0.2
+ Fold09.Rep5: alpha=1.0, lambda=0.2
- Fold09.Rep5: alpha=1.0, lambda=0.2
+ Fold10.Rep5: alpha=0.0, lambda=0.2
- Fold10.Rep5: alpha=0.0, lambda=0.2
+ Fold10.Rep5: alpha=0.1, lambda=0.2
- Fold10.Rep5: alpha=0.1, lambda=0.2
+ Fold10.Rep5: alpha=0.2, lambda=0.2
- Fold10.Rep5: alpha=0.2, lambda=0.2
+ Fold10.Rep5: alpha=0.4, lambda=0.2
- Fold10.Rep5: alpha=0.4, lambda=0.2
+ Fold10.Rep5: alpha=0.6, lambda=0.2
- Fold10.Rep5: alpha=0.6, lambda=0.2
+ Fold10.Rep5: alpha=0.8, lambda=0.2
- Fold10.Rep5: alpha=0.8, lambda=0.2
+ Fold10.Rep5: alpha=1.0, lambda=0.2
- Fold10.Rep5: alpha=1.0, lambda=0.2
Aggregating results
Selecting tuning parameters
Fitting alpha = 0.1, lambda = 0.0587 on full training set
glmnetFitglmnet
891 samples
53 predictor
2 classes: 'Survived', 'Dead'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 5 times)
Summary of sample sizes: 803, 802, 802, 802, 802, 802, ...
Addtional sampling using SMOTE
Resampling results across tuning parameters:
alpha lambda ROC Sens Spec
0.0 0.01000000 0.8624351 0.7269580 0.8875152
0.0 0.01487179 0.8624351 0.7269580 0.8875152
0.0 0.01974359 0.8624351 0.7269580 0.8875152
0.0 0.02461538 0.8624351 0.7269580 0.8875152
0.0 0.02948718 0.8624351 0.7269580 0.8875152
0.0 0.03435897 0.8626287 0.7269580 0.8871515
0.0 0.03923077 0.8629192 0.7269412 0.8871515
0.0 0.04410256 0.8631763 0.7269076 0.8860539
0.0 0.04897436 0.8633421 0.7263361 0.8856902
0.0 0.05384615 0.8631602 0.7246218 0.8860539
0.0 0.05871795 0.8632236 0.7234286 0.8856902
0.0 0.06358974 0.8634279 0.7240168 0.8853266
0.0 0.06846154 0.8634157 0.7240168 0.8838721
0.0 0.07333333 0.8634152 0.7246218 0.8835017
0.0 0.07820513 0.8633267 0.7252269 0.8838653
0.0 0.08307692 0.8633870 0.7246723 0.8842290
0.0 0.08794872 0.8632672 0.7264370 0.8838653
0.0 0.09282051 0.8630221 0.7258487 0.8838653
0.0 0.09769231 0.8630112 0.7246723 0.8835017
0.0 0.10256410 0.8630108 0.7258487 0.8827744
0.0 0.10743590 0.8629793 0.7270252 0.8820337
0.0 0.11230769 0.8629057 0.7275966 0.8820337
0.0 0.11717949 0.8629160 0.7264370 0.8816700
0.0 0.12205128 0.8629367 0.7252605 0.8816700
0.0 0.12692308 0.8628505 0.7246723 0.8813064
0.0 0.13179487 0.8627742 0.7252437 0.8813064
0.0 0.13666667 0.8626347 0.7240840 0.8816700
0.0 0.14153846 0.8626660 0.7234790 0.8816700
0.0 0.14641026 0.8624861 0.7228908 0.8816700
0.0 0.15128205 0.8623359 0.7205378 0.8809360
0.0 0.15615385 0.8622288 0.7211261 0.8805724
0.0 0.16102564 0.8621643 0.7211092 0.8798451
0.0 0.16589744 0.8620677 0.7199496 0.8791111
0.0 0.17076923 0.8620130 0.7193782 0.8787475
0.0 0.17564103 0.8620221 0.7193782 0.8791111
0.0 0.18051282 0.8620338 0.7193782 0.8791111
0.0 0.18538462 0.8620142 0.7193782 0.8780202
0.0 0.19025641 0.8620445 0.7182017 0.8765657
0.0 0.19512821 0.8619043 0.7170252 0.8765657
0.0 0.20000000 0.8618487 0.7152773 0.8758384
0.1 0.01000000 0.8600372 0.7241176 0.8878519
0.1 0.01487179 0.8615477 0.7264538 0.8896768
0.1 0.01974359 0.8624891 0.7241345 0.8875017
0.1 0.02461538 0.8631904 0.7223866 0.8878653
0.1 0.02948718 0.8635738 0.7270252 0.8871246
0.1 0.03435897 0.8640134 0.7281513 0.8867542
0.1 0.03923077 0.8642901 0.7257983 0.8860269
0.1 0.04410256 0.8644447 0.7257983 0.8852997
0.1 0.04897436 0.8644893 0.7246218 0.8852997
0.1 0.05384615 0.8647542 0.7234622 0.8845724
0.1 0.05871795 0.8648624 0.7205378 0.8849360
0.1 0.06358974 0.8646418 0.7205378 0.8849360
0.1 0.06846154 0.8645368 0.7211261 0.8849360
0.1 0.07333333 0.8643896 0.7193950 0.8842020
0.1 0.07820513 0.8643594 0.7176303 0.8842020
0.1 0.08307692 0.8642748 0.7141008 0.8845589
0.1 0.08794872 0.8640778 0.7135294 0.8841953
0.1 0.09282051 0.8636771 0.7123697 0.8834815
0.1 0.09769231 0.8635362 0.7129580 0.8823906
0.1 0.10256410 0.8631628 0.7123697 0.8805657
0.1 0.10743590 0.8628222 0.7123529 0.8798316
0.1 0.11230769 0.8625770 0.7106050 0.8794680
0.1 0.11717949 0.8622052 0.7111933 0.8780135
0.1 0.12205128 0.8619550 0.7111933 0.8772795
0.1 0.12692308 0.8616472 0.7100336 0.8765522
0.1 0.13179487 0.8616144 0.7088908 0.8761886
0.1 0.13666667 0.8614748 0.7083025 0.8747340
0.1 0.14153846 0.8612211 0.7082857 0.8747340
0.1 0.14641026 0.8611024 0.7076975 0.8743636
0.1 0.15128205 0.8609678 0.7065210 0.8740000
0.1 0.15615385 0.8605709 0.7059328 0.8732727
0.1 0.16102564 0.8604414 0.7059496 0.8725455
0.1 0.16589744 0.8602385 0.7048067 0.8721751
0.1 0.17076923 0.8599341 0.7036471 0.8725387
0.1 0.17564103 0.8598033 0.7042353 0.8718114
0.1 0.18051282 0.8595210 0.7036639 0.8707205
0.1 0.18538462 0.8591656 0.7024874 0.8703569
0.1 0.19025641 0.8589091 0.7018992 0.8699933
0.1 0.19512821 0.8584617 0.7013277 0.8703569
0.1 0.20000000 0.8581420 0.7013277 0.8692660
0.2 0.01000000 0.8591220 0.7280672 0.8882020
0.2 0.01487179 0.8604820 0.7245882 0.8903906
0.2 0.01974359 0.8618651 0.7228571 0.8911178
0.2 0.02461538 0.8620406 0.7228908 0.8892997
0.2 0.02948718 0.8620432 0.7217143 0.8889360
0.2 0.03435897 0.8622358 0.7234958 0.8889428
0.2 0.03923077 0.8622652 0.7223361 0.8885859
0.2 0.04410256 0.8620627 0.7229412 0.8889495
0.2 0.04897436 0.8620116 0.7188235 0.8874949
0.2 0.05384615 0.8619238 0.7153109 0.8864040
0.2 0.05871795 0.8619083 0.7147227 0.8856700
0.2 0.06358974 0.8614219 0.7141513 0.8853064
0.2 0.06846154 0.8613769 0.7094790 0.8845724
0.2 0.07333333 0.8613267 0.7089076 0.8849360
0.2 0.07820513 0.8613301 0.7071429 0.8842088
0.2 0.08307692 0.8613348 0.7065546 0.8823906
0.2 0.08794872 0.8612440 0.7036471 0.8805724
0.2 0.09282051 0.8607954 0.7036639 0.8791111
0.2 0.09769231 0.8604293 0.7007563 0.8765657
0.2 0.10256410 0.8599402 0.7007563 0.8747475
0.2 0.10743590 0.8591161 0.7025378 0.8736431
0.2 0.11230769 0.8580974 0.7019496 0.8721818
0.2 0.11717949 0.8576971 0.7025042 0.8710774
0.2 0.12205128 0.8571679 0.7025042 0.8703502
0.2 0.12692308 0.8567174 0.7024874 0.8688956
0.2 0.13179487 0.8561708 0.7007395 0.8656229
0.2 0.13666667 0.8550908 0.7013277 0.8648956
0.2 0.14153846 0.8544119 0.7007395 0.8645320
0.2 0.14641026 0.8540218 0.7007563 0.8641684
0.2 0.15128205 0.8534856 0.7007563 0.8638047
0.2 0.15615385 0.8528506 0.7001681 0.8627138
0.2 0.16102564 0.8523328 0.6984034 0.8627138
0.2 0.16589744 0.8514521 0.6984034 0.8619865
0.2 0.17076923 0.8508972 0.6978151 0.8598047
0.2 0.17564103 0.8506594 0.6972269 0.8594411
0.2 0.18051282 0.8500216 0.6978151 0.8583434
0.2 0.18538462 0.8495585 0.6972437 0.8572458
0.2 0.19025641 0.8490212 0.6966723 0.8561549
0.2 0.19512821 0.8488122 0.6955126 0.8557912
0.2 0.20000000 0.8477981 0.6949244 0.8547003
0.4 0.01000000 0.8608629 0.7170924 0.8856835
0.4 0.01487179 0.8630875 0.7188235 0.8875017
0.4 0.01974359 0.8635522 0.7235126 0.8882290
0.4 0.02461538 0.8638852 0.7241008 0.8863973
0.4 0.02948718 0.8640961 0.7246387 0.8874882
0.4 0.03435897 0.8636815 0.7205882 0.8878519
0.4 0.03923077 0.8634645 0.7188403 0.8867542
0.4 0.04410256 0.8625804 0.7165210 0.8845589
0.4 0.04897436 0.8610777 0.7129916 0.8845589
0.4 0.05384615 0.8603462 0.7106218 0.8812727
0.4 0.05871795 0.8593766 0.7100168 0.8783569
0.4 0.06358974 0.8586905 0.7106387 0.8758047
0.4 0.06846154 0.8577189 0.7083193 0.8739865
0.4 0.07333333 0.8561190 0.7071765 0.8707138
0.4 0.07820513 0.8539432 0.7042521 0.8692525
0.4 0.08307692 0.8520797 0.7048571 0.8674276
0.4 0.08794872 0.8510941 0.7054454 0.8652391
0.4 0.09282051 0.8498438 0.7060168 0.8612391
0.4 0.09769231 0.8489821 0.7042521 0.8590572
0.4 0.10256410 0.8485292 0.7054286 0.8543232
0.4 0.10743590 0.8478570 0.7042521 0.8535960
0.4 0.11230769 0.8474843 0.7013277 0.8514074
0.4 0.11717949 0.8474952 0.7001345 0.8514141
0.4 0.12205128 0.8472912 0.6995462 0.8503232
0.4 0.12692308 0.8465502 0.6989580 0.8499596
0.4 0.13179487 0.8458615 0.6977983 0.8495960
0.4 0.13666667 0.8460465 0.6960336 0.8499596
0.4 0.14153846 0.8456487 0.6936807 0.8510640
0.4 0.14641026 0.8457106 0.6919328 0.8510640
0.4 0.15128205 0.8463243 0.6884034 0.8514276
0.4 0.15615385 0.8466792 0.6872269 0.8514276
0.4 0.16102564 0.8467969 0.6860840 0.8514276
0.4 0.16589744 0.8465443 0.6860840 0.8517912
0.4 0.17076923 0.8460047 0.6854958 0.8521549
0.4 0.17564103 0.8456889 0.6843193 0.8521549
0.4 0.18051282 0.8452321 0.6843193 0.8521549
0.4 0.18538462 0.8443937 0.6831765 0.8525185
0.4 0.19025641 0.8439948 0.6831765 0.8525185
0.4 0.19512821 0.8432182 0.6831765 0.8525185
0.4 0.20000000 0.8429886 0.6820000 0.8525185
0.6 0.01000000 0.8615146 0.7217311 0.8889428
0.6 0.01487179 0.8625244 0.7234118 0.8911380
0.6 0.01974359 0.8627385 0.7234622 0.8911246
0.6 0.02461538 0.8630850 0.7211765 0.8900202
0.6 0.02948718 0.8622451 0.7194286 0.8860000
0.6 0.03435897 0.8620347 0.7135966 0.8830842
0.6 0.03923077 0.8608737 0.7077311 0.8808956
0.6 0.04410256 0.8595263 0.7077647 0.8776229
0.6 0.04897436 0.8580028 0.7071765 0.8750774
0.6 0.05384615 0.8563695 0.7054454 0.8725253
0.6 0.05871795 0.8546962 0.7048739 0.8641616
0.6 0.06358974 0.8528297 0.7066050 0.8601549
0.6 0.06846154 0.8511820 0.7071765 0.8568822
0.6 0.07333333 0.8498130 0.7089244 0.8521481
0.6 0.07820513 0.8489291 0.7083361 0.8488687
0.6 0.08307692 0.8492258 0.7071597 0.8488620
0.6 0.08794872 0.8491236 0.7054118 0.8488620
0.6 0.09282051 0.8492666 0.7018824 0.8488620
0.6 0.09769231 0.8486469 0.7001176 0.8492256
0.6 0.10256410 0.8485608 0.6954790 0.8506869
0.6 0.10743590 0.8486213 0.6937143 0.8506869
0.6 0.11230769 0.8478946 0.6931261 0.8506869
0.6 0.11717949 0.8466370 0.6901849 0.8506869
0.6 0.12205128 0.8461776 0.6860840 0.8517912
0.6 0.12692308 0.8455451 0.6860840 0.8517912
0.6 0.13179487 0.8436514 0.6854958 0.8517912
0.6 0.13666667 0.8430642 0.6837647 0.8521549
0.6 0.14153846 0.8423842 0.6831765 0.8521549
0.6 0.14641026 0.8418510 0.6825882 0.8525185
0.6 0.15128205 0.8407780 0.6820000 0.8525185
0.6 0.15615385 0.8404541 0.6814118 0.8525185
0.6 0.16102564 0.8401007 0.6814118 0.8525185
0.6 0.16589744 0.8396466 0.6814118 0.8525185
0.6 0.17076923 0.8391832 0.6814118 0.8525185
0.6 0.17564103 0.8392284 0.6814118 0.8525185
0.6 0.18051282 0.8390840 0.6814118 0.8525185
0.6 0.18538462 0.8388220 0.6814118 0.8525185
0.6 0.19025641 0.8389099 0.6814118 0.8525185
0.6 0.19512821 0.8367532 0.6814118 0.8525185
0.6 0.20000000 0.8350971 0.6814118 0.8525185
[ reached getOption("max.print") -- omitted 80 rows ]
ROC was used to select the optimal model using the largest value.
The final values used for the model were alpha = 0.1 and lambda = 0.05871795.
plot(glmnetFit)re <- resamples(x = list(xgb=xgbFit,xgbsmote=xgbsmoteFit,rf=rfFit.y,rfsmote=rfsmoteFit.y,gbm=boostFit))
summary(re)
Call:
summary.resamples(object = re)
Models: xgb, xgbsmote, rf, rfsmote, gbm
Number of resamples: 50
ROC
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
xgb 0.7360963 0.8603304 0.8925134 0.8839215 0.9196505 0.9540260 0
xgbsmote 0.7462567 0.8653743 0.8867647 0.8800014 0.9078877 0.9350267 0
rf 0.7807487 0.8622670 0.8984301 0.8885360 0.9126833 0.9556150 0
rfsmote 0.7759358 0.8581818 0.8880026 0.8831274 0.9106551 0.9574866 0
gbm 0.7604278 0.8570834 0.8848587 0.8817104 0.9055828 0.9609626 0
Sens
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
xgb 0.5588235 0.7058824 0.7352941 0.7388908 0.7941176 0.9142857 0
xgbsmote 0.4411765 0.5882353 0.6470588 0.6496134 0.7058824 0.8529412 0
rf 0.5294118 0.6764706 0.7352941 0.7286891 0.7941176 0.8823529 0
rfsmote 0.5294118 0.6470588 0.6857143 0.6983025 0.7647059 0.9117647 0
gbm 0.5588235 0.6907563 0.7647059 0.7532269 0.8235294 0.9117647 0
Spec
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
xgb 0.7777778 0.8709596 0.8898990 0.8844916 0.9090909 0.9636364 0
xgbsmote 0.7818182 0.8909091 0.9272727 0.9169630 0.9454545 1.0000000 0
rf 0.8181818 0.8727273 0.8909091 0.8940202 0.9259259 0.9818182 0
rfsmote 0.8181818 0.8727273 0.8909091 0.9016498 0.9272727 0.9818182 0
gbm 0.8181818 0.8545455 0.8898990 0.8849091 0.9217172 0.9818182 0
bwplot(re)compare_models(xgbFit,rfFit.y,metric = 'ROC')
One Sample t-test
data: x
t = -0.57365, df = 49, p-value = 0.5688
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.02077988 0.01155087
sample estimates:
mean of x
-0.004614506
summary(diff(re))
Call:
summary.diff.resamples(object = diff(re))
p-value adjustment: bonferroni
Upper diagonal: estimates of the difference
Lower diagonal: p-value for H0: difference = 0
ROC
xgb xgbsmote rf rfsmote gbm
xgb 0.0039200 -0.0046145 0.0007941 0.0022111
xgbsmote 1.00000 -0.0085345 -0.0031259 -0.0017090
rf 1.00000 1.00000 0.0054086 0.0068256
rfsmote 1.00000 1.00000 0.03412 0.0014169
gbm 1.00000 1.00000 0.16388 1.00000
Sens
xgb xgbsmote rf rfsmote gbm
xgb 0.08928 0.01020 0.04059 -0.01434
xgbsmote 7.907e-05 -0.07908 -0.04869 -0.10361
rf 1.0000000 7.109e-05 0.03039 -0.02454
rfsmote 0.2697866 0.0243587 0.0001129 -0.05492
gbm 1.0000000 7.618e-07 0.0116300 6.092e-07
Spec
xgb xgbsmote rf rfsmote gbm
xgb -0.0324714 -0.0095286 -0.0171582 -0.0004175
xgbsmote 0.009900 0.0229428 0.0153131 0.0320539
rf 1.000000 0.290902 -0.0076296 0.0091111
rfsmote 0.335682 1.000000 0.153052 0.0167407
gbm 1.000000 0.018616 0.236054 0.005096
test.imp <- test.raw
#Embarked
test.imp$Embarked[is.na(test.imp$Embarked)]='S'
#Title
test.raw$title <- str_extract(pattern = '[a-zA-Z]+(?=\\.)',string = test.raw$Name)
#test.raw$title <- as.factor(test.raw$title)
test.imp$title <- as.character(test.raw$title)
test.imp$title[test.imp$title %in% c('Capt','Col','Major')] <- 'Officer'
test.imp$title[test.imp$title %in% c('Don','Dr','Rev','Sir','Jonkheer','Countess','Lady','Dona')] <- 'Royalty'
test.imp$title[test.imp$title %in% c('Mrs','Mme')] <- 'Mrs'
test.imp$title[test.imp$title %in% c('Ms','Mlle')] <- 'Miss'
test.imp$title <- as.factor(test.imp$title)
#Missing age
missing.age <- test.imp %>% filter(is.na(Age))
age.predicted <- predict(rfFit, newdata = missing.age)
test.imp$Age[is.na(test.imp$Age)] <- age.predicted
test.imp$Age[test.imp$title=='Master' & test.imp$Age > 20] <- 4
#Child
test.imp$child <- 0
test.imp$child[test.imp$Age<18] <- 1
test.imp$almostadult <- as.numeric(between(test.imp$Age,16,18))
#Young/old
test.imp$Young <- ifelse(test.imp$Age<10,1,0)
test.imp$Seniors <- ifelse(test.imp$Age>60,1,0)
#Family Related
test.imp$TotalFam <- test.imp$SibSp + test.imp$Parch + 1
test.imp$Name <- NULL
test.imp$LargeParCh <- as.numeric(test.imp$Parch>=3)
test.imp$LargeSibSp <- as.numeric(test.imp$SibSp>=3)
test.imp$Single <- ifelse(test.imp$TotalFam==1,1,0)
test.imp$Couple <- ifelse(test.imp$TotalFam==2,1,0)
test.imp$Family <- ifelse(test.imp$TotalFam>2,1,0)
#Cabin & Deck
test.imp$CabinMissing <- as.numeric(is.na(test.raw$Cabin))
test.imp$CabinCode <- map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '')[[1]][1])
test.imp$CabinCode[is.na(test.imp$CabinCode)] <- 'U'
test.imp$CabinNum <- as.numeric(map_chr(test.raw$Cabin,~str_split(string = .x,pattern = '[a-zA-Z]')[[1]][2]))
test.imp$CabinNum <- map_int(test.imp$CabinNum, ~as.integer(str_split(.x,pattern = '',simplify = T)[1][1]))
test.imp$CabinNum[is.na(test.imp$CabinNum)] <- 0
test.imp$TopDeck <- ifelse(test.imp$CabinCode %in% c('A','B'),1,0)
test.imp$MidDeck <- ifelse(test.imp$CabinCode %in% c('C','D'),1,0)
test.imp$LowerDeck <- ifelse(test.imp$TopDeck==0 & test.imp$MidDeck ==0 ,1,0)
test.imp$NumberofCabins <- map_int(test.raw$Cabin,~str_split(string = .x,pattern = ' ')[[1]] %>% length)
test.imp$Cabin <- NULL
# Ticket
test.imp %<>%
mutate(
Ticket = str_to_upper(Ticket) %>%
str_replace_all(pattern = regex(pattern = '[.\\/]'),replacement = ''),
TicketNum = str_extract(Ticket,pattern = regex('([0-9]){3,}')),
TicketNumStart = map_int(TicketNum,~as.integer(str_split(.x,pattern = '',simplify = T)[1])),
TicketNumLen = map_int(TicketNum,~dim(str_split(.x,pattern = '',simplify = T))[2]),
TicketChar = str_extract(Ticket,pattern = regex('^[a-zA-Z/\\.]+'))
) %>%
mutate(
TicketChar = ifelse(is.na(TicketChar),'U',TicketChar),
TicketNumStart = ifelse(is.na(TicketNumStart),0,TicketNumStart),
TicketNumLen = ifelse(is.na(TicketNumLen),0,TicketNumLen),
)
test.imp$Ticket <- NULL
test.imp$TicketNum <- NULL
#Fare
test.imp$Fare[is.na(test.imp$Fare)] <- 14.4542
test.imp$Fare[test.imp$Fare>232] <- 232# boostPred <- predict(object = boostFit,
# newdata = test.imp)
dumV <- dummyVars(formula = ~.,data = test.imp)
Dtest <- predict(dumV,test.imp)
xgbPred <- predict(object = xgbFit,
newdata = Dtest)
rfPred <- predict(object = rfFit.y,
newdata = test.imp)Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) :
factor TicketChar has new levels AQ, LP, STONOQ
PID <-
readData(Titanic.path,
test.data.file,
test.column.types,
missing.types)
PID <- PID$PassengerId
output <- write.csv(
x = data.frame(
PassengerId = PID,
Survived = as.numeric(xgbPred)*-1+2
),
file = 'aug30.csv',
row.names = F
)I think this approach depends on the academic background and the industry of the analyst. Prof Srinivasan, and my mentor at work both have strong statistical academic backgrounds, and both believe in thorough EDA of the data. I’ve also noticed this approach from individuals in the banking & insurance industry - perhaps due to regulatory requirements. On the other hand, folks trained in computer science and algorithmic data science tend to underplay the importance of thorough EDA.↩
To iterate variable names in ggplot, use ggplot(...)+aes_string(...) in place of ggplot(...,aes(...)).↩
Read more about beanplots here: https://cran.r-project.org/web/packages/beanplot/vignettes/beanplot.pdf↩